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					Insurers: Beyond Transactions, What’s Your People Policy?
Insurers: Beyond Transactions, What’s Your People Policy?
The insurance industry is filled with potential, but companies remain stuck in old ways, leaving customers unhappy and disconnected. One in three customers switched providers last year, due to unsatisfactory insurance customer experiences. At LIKE.TG, we ask, “What do people truly want when they interact with their insurance company?” Extensive research shows that customers, both new and old, want more than just coverage and affordability. They seek understanding, simplicity, and empathy. Only 43% of customers say their insurer anticipates their needs, a disappointing statistic considering evolving expectations. Let’s look at this through the lens of a typical customer. Meet Natalie, a physical therapist and mother, who’s budget-conscious, comfortable with technology, and who values human connection when engaging with businesses, no matter the size. We’ll trace her path to understand where she feels supported and, more importantly, where she feels lost while navigating her insurance journey. Our goal is to examine how we might reimagine the insurance customer experience in a way that speaks to her core needs and desires, inspiring transformation along the way. Let’s begin. Stage 1: Searching for security In this opening stage, Natalie feels anxious but hopeful as she begins evaluating her options for how to best protect her home and automobiles. Her discovery can either empower her with clarity and conviction or leave her feeling overwhelmed and uncertain. As a discerning consumer, Natalie has the grit to push past the noise to find the right solution, but insurance companies vying for her business must challenge themselves to deliver a helpful, seamless experience. Today’s customer experience, with its numerous touchpoints and channels, can present several challenges for individuals like Natalie as they start to explore their options and evaluate potential solutions. A majority of insurers use three or more systems for client engagement, frustrating customers as they encounter repetitive tasks across different channels. This often leads to key data and information getting duplicated or lost in the shuffle. For the insurer, this puts an unnecessary administrative burden on their employees, who should be focused on building relationships and delivering value. Tomorrow’s insurance customer experience: At LIKE.TG, we’re helping design an entirely new insurance customer experience with intelligent systems and automation. As Natalie evaluates her options, her information and activity are discreetly monitored and used for context. This advantages both the end customer and the agent. It ensures a thoughtful experience by recalling Natalie’s journey and providing tailored guidance and reduced friction, while also helping agents know when to prioritise her for personal follow-up. Overall, the streamlined approach empowers agents to excel in relationship-building while also boosting productivity. Stage 2: Getting to know the insurer After choosing an insurer, Natalie enters the onboarding phase, where initial interactions set the tone of her relationship. Carefully designed onboarding can instill trust and reassurance. At this stage, insurance companies should lead with thoughtfulness around the reasons for which the policy was purchased, going beyond transaction talk. Onboarding is not a checkbox to be marked complete; it is an opportunity to demonstrate the company values while creating moments to understand what customers like Natalie expect from the insurer. Insurance onboarding frequently amounts to a series of decontextualised emails, overwhelming new customers with policy minutiae, bundling promotions, add-on features, loyalty programs, and app downloads. The communications can feel impersonal and poorly timed – a missed opportunity and a significant shortcoming of today’s customer experience since the onboarding window is when Natalie will be most attentive and engaged. Tomorrow’s insurance customer experience: The future of onboarding will vastly improve. Insurers will simplify the process for customers like Natalie and provide only essential information. Onboarding that keeps customer needs at the core can help obtain meaningful consent and establish trust and transparency around data practices. This, coupled with the harmonisation of engagement, behavioural signals, and third-party data will help insurers anticipate Natalie’s needs, resulting in her feeling connected and supported in her interactions. With a renewed understanding that onboarding is an ongoing practice, insurers will only introduce new offers and programs when the moment is right. If six months in, Natalie adds a new young driver to her policy, an insurer might seize the opportunity to promote a young driver discount program. Activating a data-driven approach can create a trusted environment where customers like Natalie share more data because they feel heard, understood, and helped. By understanding this ‘trust’-oriented purpose behind the data model, insurers can make informed decisions about what data to collect and how to use it, ultimately leading to better outcomes for both the customer and the organisation. Personalise your customer journey Learn how to manage, track, and automate customer interactions with smarter technology. This Trail is a helpful learning module that can help you get started on the free online learning platform by LIKE.TG. Take the Trail +2900 points Trail Deliver Personalised Insurance Service with Financial Services Cloud Learn the ways of this trail. Stage 3: Filing a claim When the unexpected strikes, Natalie may need to submit a claim. How efficiently and compassionately her insurer handles this process can impact her overall satisfaction and trust in the company. A claim signals uncharted territory where Natalie feels vulnerable and relies on guidance. Now is the time for companies to demonstrate their values in action. When an incident occurs today, Natalie will submit a claim amid stress and uncertainty – only to face disjointed, manual processes that exacerbate her challenges. While some insurance companies have modernised their claims experience, much of the industry lags, resulting in today’s fragmented experiences marked by delays, opacity, and eroded trust. Get articles selected just for you, in your inbox Sign up now Purpose-driven insurers view claims as an opportunity to provide comfort and care when customers feel most vulnerable. They recognise that efficient claims fuel trust and loyalty more than marketing ever could. By leveraging existing data and new technologies, insurers can transform a traditionally tedious and anxiety-inducing process into a streamlined, personalised journey. Tomorrow’s insurance customer experience: Say Natalie previously enrolled in a usage-based auto insurance program to help manage costs and reward conscientious driving habits. The same telematics providing safe driving discounts can help expedite her claim. When an accident occurs, her location, speed, and impact data can be instantly shared, enabling insurers to proactively reach out, respond to the situation, and accelerate claim filing. For the policyholder, this means less stress during a difficult time and greater trust. The claims process, though rarely enjoyable, can at least be hassle-free. This future experience, which is already here for our customers at LIKE.TG, is a powerful convergence of AI, data, and trust, underpinned by a foundation of customer-centricity – all in one tool. Stage 4: Ongoing assurance Regular interactions with the insurance company can either deepen Natalie’s engagement and loyalty or leave her feeling detached and uninformed. In today’s customer experience, it is not uncommon for policyholders to only hear from their insurers during significant milestones such as policy issuance, billing, or claims. These interactions are often transactional in nature, focusing on the functional aspects of the policy rather than building a relationship with the customer. Tomorrow’s insurance customer experience: Natalie’s future experience is proactive and emotionally intelligent. Say she lives in a climate hazard zone – subject to hurricanes, floods, or fires. Because her insurer has a firm grasp on the risk she faces, she consistently receives prevention guidance and personalised offers to enhance protection. When disaster strikes, outreach is immediate, empathetic, and supportive. Natalie knows her insurer has her back and that she can trust them as an advisor who delivers tailored recommendations, anticipatory guidance, and compassionate care. The ingredients are all there: rich data, smart technology, and most importantly, human-centered strategy. Now insurers must combine them to design powerful insurance customer experiences that put people first.
 Amazon Redshift Vs Athena: Compare On 7 Key Factors
Amazon Redshift Vs Athena: Compare On 7 Key Factors
In the Data Warehousing and Business Analysis environment, growing businesses have a rising need to deal with huge volumes of data. In cases like this, key stakeholders often debate on whether to go with Redshift or with Athena – two of the big names that help seamlessly handle large chunks of data. This blog aims to ease this dilemma by providing a detailed comparison of Redshift Vs Athena.Although both the services are designed for Analytics, both the services provide different features and optimize for different use cases. This blog covers the following: Amazon Redshift Vs Athena – Brief Overview Amazon Redshift Overview Amazon Redshift is a fully managed, petabyte data warehouse service over the cloud. Redshift data warehouse tables can be connected using JDBC/ODBC clients or through the Redshift query editor. Redshift comprises Leader Nodes interacting with Compute nodes and clients. Clients can only interact with a Leader node. Compute nodes can have multiple slices. Slices are nothing but virtual CPUs Athena Overview Amazon Athena is a serverless Analytics service to perform interactive queries over AWS S3. Since Athena is a serverless service, the user or Analyst does not have to worry about managing any infrastructure. Athena query DDLs are supported by Hive and query executions are internally supported by Presto Engine. Athena only supports S3 as a source for query executions. Athena supports almost all the S3 file formats to execute the query. Athena is well integrated with AWS Glue Crawler to devise the table DDLs Redshift Vs Athena Comparison Feature Comparison Amazon Redshift Features Redshift is purely an MPP data warehouse application service used by the Analyst or Data warehouse engineer who can query the tables. The tables are in columnar storage format for fast retrieval of data. You can watch a short intro on Redshift here: Data is stored in the nodes and when the Redshift users hit the query in the client/query editor, it internally communicates with Leader Node. The leader node internally communicates with the Compute node to retrieve the query results. In Redshift, both compute and storage layers are coupled, however in Redshift Spectrum, compute and storage layers are decoupled. Athena Features Athena is a serverless analytics service where an Analyst can directly perform the query execution over AWS S3. This service is very popular since this service is serverless and the user does not have to manage the infrastructure. Athena supports various S3 file-formats including CSV, JSON, parquet, orc, and Avro. Along with this Athena also supports the Partitioning of data. Partitioning is quite handy while working in a Big Data environment Redshift Vs Athena – Feature Comparison Table Scope of Scaling Both Redshift and Athena have an internal scaling mechanism. Get the best content from the world of data science in your inbox once a month.Thank you for Subscribing to our Newsletter! Amazon Redshift Scaling Since data is stored inside the node, you need to be very careful in terms of storage inside the node. While managing the cluster, you need to define the number of nodes initially. Once the cluster is ready with a specific number of nodes, you can reduce or increase the nodes. Redshift provides 2 kinds of node resizing features: Elastic resize Classic resize Elastic Resize Elastic resize is the fasted way to resize the cluster. In the elastic resize, the cluster will be unavailable briefly. This often happens only for a few minutes. Redshift will place the query in a paused state temporarily. However, this resizing feature has a drawback as it supports a resizing in multiples of 2 (for dc2.large or ds2.xlarge cluster) ie. 2 node clusters changed to 4 or a 4 node cluster can be reduced to 2, etc. Also, you cannot modify a dense compute node cluster to dense storage or vice versa. This resize method only supports VPC platform clusters. Classic Resize Classic resize is a slower way of resizing a cluster. Your cluster will be in a read-only state during the resizing period. This operation may take a few hours to days depending upon the actual data storage size. For classic resize you should take a snapshot of your data before the resizing operation. Workaround for faster resize -> If you want to increase 4 node cluster to 10 node cluster, perform classic resize to 5 node cluster and then use elastic resize to increase 10 node cluster for faster resizing. Athena Scaling Being a serverless service, you do not have to worry about scaling in Athena. AWS manages the scaling of your Athena infrastructure. However, there is a limit on the number of queries, databases defined by AWS ie. number of concurrent queries, the number of databases per account/role, etc. Ease of Data Replication Amazon Redshift – Ease of Data Replication In Redshift, there is a concept of the Copy command. Using the Copy command, data can be loaded into Redshift from S3, Dynamodb, or EC2 instances. Although the Copy command is for fast loading it will work at its best when all the slices of nodes equally participate in the copy command Download the Guide to Select the Right Data Warehouse Learn the key factors you should consider while selecting the right data warehouse for your business. Below is an example: copy table from 's3://<your-bucket-name>/load/key_prefix' credentials 'aws_access_key_id=<Your-Access-Key-ID>;aws_secret_access_key=<Your-Secret-Access-Key>' Options; You can load multiple files in parallel so that all the slices can participate. For the COPY command to work efficiently, it is recommended to have your files divided into equal sizes of 1 MB – 1 GB after compression. For example, if you are trying to load a file of 2 GB into DS1.xlarge cluster, you can divide the file into 2 parts of 1 GB each after compression so that all the 2 slices of DS1.xlarge can participate in parallel. Please refer to AWS documentation to get the slice information for each type of Redshift node. Using Redshift Spectrum, you can further leverage the performance by keeping cold data in S3 and hot data in the Redshift cluster. This way you can further improve your performance. In case you are looking for a much easier and seamless means to load data to Redshift, you can consider fully managed Data Integration Platforms such as LIKE.TG . LIKE.TG helps load data from any data source to Redshift in real-time without having to write any code. Athena – Ease of Data Replication Since Athena is an Analytical query service, you do not have to move the data into Data Warehouse. You can directly query your data over S3 and this way you do not have to worry about node management, loading the data, etc. Data Storage Formats Supported by Redshift and Athena Redshift data warehouse only supports structured data at the node level. However, Redshift Spectrum tables do also support other storage formats ie. parquet, orc, etc. On the other hand, Athena supports a large number of storage formats ie. parquet, orc, Avro, JSON, etc. It also has a feature called Glue classifier. Athena is well integrated with AWS Glue. Athena table DDLs can be generated automatically using Glue crawlers too. Glue has saved a lot of significant manual tasks of writing manual DDL or defining the table structure manually. In Glue, there is a feature called a classifier. Using the Glue classifier, you can make Athena support a custom file type. This is a much better feature that made Athena quite handy dealing in with almost all the types of file formats. Data Warehouse Performance Redshift Data Warehouse Performance The performance of the data warehouse application is solely dependent on the way your cluster is defined. In Redshift, there is a concept of Distribution key and Sort key. The distribution key defines the way how your data is distributed inside the node. The distribution key drives your query performance during the joins. Sort key defines the way data is stored in the blocks. The more the data is in sorted order the faster the performance of your query will be. Sort key can be termed as a replacement for an index in other MPP data warehouses. Sort keys are primarily taken into effect during the filter operations. There are 2 types of sort keys (Compound sort keys and Interleaved sort keys). In compound sort keys, the sort keys columns get the weight in the order the sort keys columns are defined. On the other hand in the compound sort key, all the columns get equal weightage. Interleaved sort keys are typically used when multiple users are using the same query but are unsure of the filter condition Another important performance feature in Redshift is the VACUUM. Bear in mind VACUUM is an I/O intensive operation and should be used during the off-business hours. However, off-late AWS has introduced the feature of auto-vacuuming however it is still advised to vacuum your tables during regular intervals. The vacuum will keep your tables sorted and reclaim the deleted blocks (For delete operations performed earlier in the cluster). You can read about Redshift VACUUM here. Athena Performance Athena Performance primarily depends on the way you hit your query. If you are querying a huge file without filter conditions and selecting all the columns, in that case, your performance might degrade. You need to be very cautious in selecting only the needful columns. You are advisable to partition your data and store your data in columnar/compressed format (ie. parquet or orc). In case you want to preview the data, better perform the limit operation else your query will take more time to execute. Example:- Select * from employee; -- High run time Select * from employee limit 10 -- better run time Amazon Redshift Vs Athena – Pricing AWS Redshift Pricing The performance of Redshift depends on the node type and snapshot storage utilized. In the case of Spectrum, the query cost and storage cost will also be added Here is the node level pricing for Redshift for the N.Virginia region (Pricing might vary based on region) AWS Athena Pricing The good part is that in Athena, you are charged only for the amount of data for which the query is scanned. Your query needs to be designed such that it does not perform unnecessary scans. As a best practice, you should compress and partition the data to save the cost significantly The usage cost of N.Virginia is $5 per TB of data scanned (The pricing might vary based on region) Along with the query scan charge, you are also charged for the data stored in S3 Architecture Athena – Architecture Athena is a serverless platform with a decoupled storage and compute architecture that allows users to query data directly in S3 without having to ingest or copy it. It is multi-tenant and uses shared resources. Users have no control over the compute resources that Athena allocates from the shared resource pool per query. Amazon Redshift Architecture The oldest architecture in the group is Redshift, which was the first Cloud DW. Its architecture was not built to separate storage and computation. While it now has RA3 nodes, which allow you to scale compute and only cache the data you need locally, it still runs as a single process. Because different workloads cannot be separated and isolated over the same data, it lags behind other decoupled storage/computing architectures. Redshift is deployed in your VPC as an isolated tenant per customer, unlike other cloud data warehouses. Scalability Athena – Scalability Athena is a multi-tenant shared resource, so there are no guarantees about the amount or availability of resources allocated to your queries. It can scale to large data volumes in terms of data volume, but large data volumes can result in very long run times and frequent time outs. The maximum number of concurrent queries is 20. Athena is probably not the best choice if scalability is a top priority. Redshift – Scalability Even with RA3, Redshift’s scale is limited because it can’t distribute different workloads across clusters. While it can automatically scale up to 10 clusters to support query concurrency, it can only handle 50 queued queries across all clusters by default. Use Cases Athena – Use Cases For Ad-Hoc analytics, Athena is a great option. Because Athena is serverless and handles everything behind the scenes, you can keep the data where it is and start querying without worrying about hardware or much else. When you need consistent and fast query performance, as well as high concurrency, it isn’t a good fit. As a result, it is rarely the best option for operational or customer-facing applications. It can also be used for batch processing, which is frequently used in machine learning applications. Redshift – -Use Cases Redshift was created to help analysts with traditional internal BI reporting and dashboard use cases. As a result, it’s commonly used as a multi-purpose Enterprise data warehouse. It can also use the AWS ML service because of its deep integrations into the AWS ecosystem, making it useful for ML projects. It is less suited for operational use cases and customer-facing use cases like Data Apps, due to the coupling of storage and compute and the difficulty in delivering low-latency analytics at scale. It’s difficult to use for Ad-Hoc analytics because of the tight coupling of storage and compute, as well as the requirement to pre-define sort and dist keys for optimal performance. Data Security Amazon Redshift – Data Security Redshift has various layers of security Cluster credential level security IAM level security Security group-level security to control the inbound rules at the port level VPC to protect your cluster by launching your cluster in a virtual networking environment Cluster encryption -> Tables and snapshots can be encrypted SSL connects can be encrypted to enforce the connection from the JDBC/ODBC SQL client to the cluster for security in transit Has facility the load and unload of the data into/from the cluster in an encrypted manner using various encryption methods It has a feature of CloudHSM. With the help of CloudHSM, you can use certificates to configure a trusted connection between Redshift and your HSM environment Athena: Data Security You can query your tables either using console or CLI Being a serverless service, AWS is responsible for protecting your infrastructure. Third-party auditors validate the security of the AWS cloud environment too. At the service level, Athena access can be controlled using IAM. Below is the encryption at rest methodologies for Athena: Service side encryption (SSE-S3) KMS encryption (SSE-KMS) Client-side encryption with keys managed by the client (CSE-KMS) Security in Transit AWS Athena uses TLS level encryption for transit between S3 and Athena as Athena is tightly integrated with S3. Query results from Athena to JDBC/ODBC clients are also encrypted using TLS. Athena also supports AWS KMS to encrypted datasets in S3 and Athena query results. Athena uses CMK (Customer Master Key) to encrypt S3 objects. Conclusion Both Redshift and Athena are wonderful services as Data Warehouse applications. If used in conjunction, it can provide great benefits. One should use Amazon Redshift when high computation is required and query large datasets and use Athena for simple queries. Share your experience of learning about Redshift vs Athena in the comments section below!
 LIKE.TG vs DMS AWS – 7 Comprehensive Parameters
LIKE.TG vs DMS AWS – 7 Comprehensive Parameters
Migrating data from different sources into Data Warehouses can be hard. Hours of engineering time need to be spent in hand-coding complex scripts to bring data into the Data Warehouse. Moreover, Data Streaming often fails due to unforeseen errors for eg. the destination is down or an error in a piece of code. With the increase in such overheads, opting for a Data Migration product becomes impertinent for smooth Data Migration.LIKE.TG Data and DMS AWS are two very effective ETL tools available in the market and users are often confused while deciding one of them. The LIKE.TG vs DMS AWS is a constant dilemma amongst the users who are looking for a hassle-free way to automate their ETL process. This post on LIKE.TG vs DMS AWS has attempted to highlight the differences between LIKE.TG and AWS Database Migration Service on a few critical parameters to help you make the right choice. Read along with the comparisons of LIKE.TG VS DMS AWS and decide which one suits you the best. Introduction to LIKE.TG Data LIKE.TG is a Unified Data Integration platform that lets you bring data into your Data Warehouse in real-time. With a beautiful interface and flawless user experience, any user can transform, enrich and clean the data and build data pipelines in minutes. Additionally, LIKE.TG also enables users to build joins and aggregates to create materialized views on the data warehouse for faster query computations. LIKE.TG also helps you to start moving data from 100+ sources to your data warehouse in real-time with no code for the price of $249/month! To learn more about LIKE.TG Data, visit here. Introduction to AWS DMS AWS DMS is a fully managed Database Migration service provided by Amazon. Users can connect various JDBC-based data sources and move the data from within the AWS console. AWS Database Migration Service allows you to migrate data from various Databases to AWS quickly and securely. The original Database remains fully functional during the migration, thereby minimizing downtime for applications that depend on the Database. To learn more about DMS AWS, visit here. Simplify your ETL Process with LIKE.TG Data LIKE.TG Datais a simple to use Data Pipeline Platform that helps you load data from100+ sourcesto any destination like Databases, Data Warehouses, BI Tools, or any other destination of your choice in real-time without having to write a single line of code. LIKE.TG provides you a hassle-free data transfer experience. Here are some more reasons why LIKE.TG is the right choice for you: Minimal Setup Time: LIKE.TG has a point-and-click visual interface that lets you connect your data source and destination in a jiffy. No ETL scripts, cron jobs, or technical knowledge is needed to get started. Your data will be moved to the destination in minutes, in real-time.Automatic Schema Mapping:Once you have connected your data source, LIKE.TG automatically detects the schema of the incoming data and maps it to the destination tables. With its AI-powered algorithm, it automatically takes care of data type mapping and adjustments – even when the schema changes at a later point.Mature Data Transformation Capability:LIKE.TG allows you to enrich, transform and clean the data on the fly using an easy Python interface. What’s more – LIKE.TG also comes with an environment where you can test the transformation on a sample data set before loading to the destination.Secure and Reliable Data Integration:LIKE.TG has a fault-tolerant architecture that ensures that the data is moved from the data source to destination in a secure, consistent and dependable manner with zero data loss.Unlimited Integrations: LIKE.TG has a large integration list for Databases, Data Warehouses, SDKs Streaming, Cloud Storage, Cloud Applications, Analytics, Marketing, and BI tools. This, in turn, makes LIKE.TG the right partner for the ETL needs of your growing organization. Try out LIKE.TG by signing up for a14-day free trial here. Comparing LIKE.TG vs DMS AWS 1) Variety of Data Source Connectors: LIKE.TG vs DMS AWS The starting point of the LIKE.TG vs DMS AWS discussion is the number of data sources these two can connect. With LIKE.TG you can migrate data from not only JDBC sources, but also from various cloud storage (Google Drive, Box, S3) SaaS (Salesforce, Zendesk, Freshdesk, Asana, etc.), Marketing systems (Google Analytics, Clevertap, Hubspot, Mixpanel, etc.) and SDKs (iOS, Android, Rest, etc.). LIKE.TG supports the migration of both structured and unstructured data. A complete list of sources supported by LIKE.TG can be found here. LIKE.TG supports all the sources supported by DMS and more. DMS, on the other hand, provides support to only JDBC databases like MySQL, PostgreSQL, MariaDB, Oracle, etc. A complete list of sources supported by DMS can be found here. However, if you need to move data from other sources like Google Analytics, Salesforce, Webhooks, etc. you would have to build and maintain complex scripts for migration to bring it into S3. From S3, DMS can be used to migrate the data to the destination DB. This would make migration a tedious two-step process. DMS does not provide support to move unstructured NoSQL data. Other noteworthy differences on the source side: LIKE.TG promises a secure SSH connection when moving data whereas DMS does not. LIKE.TG also allows users to write custom SQL to move partial data or perform table joins and aggregates on the fly while DMS does not. With LIKE.TG users can enjoy granular control on Table jobs. LIKE.TG lets you control data migration at table level allowing you to pause the data migration for certain tables in your database at will. DMS does not support such a setup. LIKE.TG allows you to move data incrementally through SQL queries and BinLog. With DMS, incremental loading of data is possible only through BinLog. 2) Data Transformations: LIKE.TG vs DMS AWS With LIKE.TG , users can Clean, Filter, Transform and Enrich both structured and unstructured data on the fly through a simple Python interface. You can even split an incoming event into multiple arbitrary events making it easy for you to normalize nested NoSQL data. All the standard Python Libraries are made available to ensure users have a hassle-free data transformation experience. The below image shows the data transformation process at LIKE.TG . DMS allows users to create basic data transformations such as Adding a prefix, Changing letters to uppercase, Skip a column, etc. However, advanced transformations like Mapping IP to location, Skipping rows based on conditions, and many others that can be easily done on LIKE.TG are not supported by DMS. The above image shows the Data transformation process of DMS AWsS. To be sure that the transformation is error-free, DMS users will have to hand-code sample event pulls and experiment on them or worse, wait for data to reach the destination to check. LIKE.TG lets users test the transformation on a sample data set and preview the result before deployment. 3) Schema handling: LIKE.TG vs DMS AWS Schemas are important for the ETL process and therefore can act as a good parameter in the LIKE.TG vs DMS discussion. LIKE.TG allows you to map the source schema to the destination schema on abeautiful visual interface. DMS does not have an interface for schema mapping. The data starts moving as soon as the job is configured. If the mapping is incorrect the task fails and someone from engineering will have to manually fix the errors. Additionally, LIKE.TG automatically detects the changing schema and notifies the user of the change so that he can take necessary action. 4) Moving Data into Redshift: LIKE.TG vs DMS AWS Amazon Redshift is a popular Data Warehouse and can act as a judging parameter in this LIKE.TG vs DMS AWS discussion. Moving Data into Redshift is a cakewalk with LIKE.TG . Users would just need to connect the sources to Redshift, write relevant transformations, and voila, data starts streaming. Moving data into Redshift through DMS comes with a lot of overheads. Users are expected to manage the S3 bucket (creating directories, managing permissions, etc.) themselves. Moreover, DMS compulsorily requires the user’s Redshift cluster region, the DMS region to be the same. While this is not a major drawback, this becomes a problem when users want to change the region of the Redshift cluster but not for S3. 5) Notifications: LIKE.TG vs DMS AWS LIKE.TG notifies all exceptions to users on both Slack and Email. The details of the exceptions are also included in the notification to enable users to take quick action. DMS notifies all the anomalies over AWS Cloudwatch only. The user will have to configure Cloudwatch to receive notifications on email. 6) Statistics and Audit log: LIKE.TG vs DMS AWS LIKE.TG provides a detailed audit log to the user to get visibility into activities that happened in the past at the user level. DMS provides logs at the task level. LIKE.TG provides a simple dashboard that provides a one-stop view of all the tasks you have created. DMS provides data migration statistics on Cloudwatch. 7) Data Modelling: LIKE.TG vs DMS AWS Data Modeling is another essential aspect of this LIKE.TG vs DMS AWS dilemma. LIKE.TG ’s Modelling and Workflows features allow you to join and aggregate the data to store results as materialized views on your destination. With these views, users experience faster query response times making any report pulls possible in a few seconds. DMS restricts its functions to data migration services only. Data Models on LIKE.TG Conclusion The article explained briefly about LIKE.TG Data and DMS AWS. It then provided a detailed discussion on the LIKE.TG vs DMS AWS choice dilemma. The article considered 7 parameters to analyze both of these ETL tools. Moreover, it provided you enough information on each criterion used in the LIKE.TG vs DMS AWS discussion. LIKE.TG Data, understand the complex processes involved in migrating your data from a source to a destination and LIKE.TG has been built just to simplify this for you. With a superior array of features as opposed to DMS, LIKE.TG ensures a hassle-free data migration experience with zero data loss. LIKE.TG Data, with its strong integration with100+ sources BI tools, allows you to export, load, transform enrich your data make it analysis-ready in a jiffy. Want to take LIKE.TG for a spin. Try LIKE.TG Data’s14 days free trialand experience the benefits! Share your views on the LIKE.TG vs DMS discussion in the comments section!
 Webhook to BigQuery: Real-time Data Streaming
Webhook to BigQuery: Real-time Data Streaming
Nowadays, streaming data is a crucial data source for any business that wants to perform real-time analytics. The first step to analyze data in real-time is to load the streaming data – often from Webhooks, in real-time to the warehouse. A common use case for a BigQuery webhook is automatically sending a notification to a service like Slack or email whenever a dataset is updated. In this article, you will learn two methods of how to load real-time streaming data from Webhook to BigQuery.Note: When architecting a Webhooks Google BigQuery integration, it’s essential to address security concerns to ensure your data remains protected. Also, when connecting BigQuery webhook, defining your webhook endpoint is essential – the address or URL that will receive the incoming data is essential. Connect Webhook to BigQuery efficiently Utilize LIKE.TG ’s pre-built webhook integration to capture incoming data streams. Configure LIKE.TG to automatically transform and load the webhook data into BigQuery tables, with no coding required. Method 1: Webhook to BigQuery using LIKE.TG Data Get Started with LIKE.TG for Free Method 2: Webhook to BigQuery ETL Using Custom Code Develop a custom application to receive and process webhook payloads. Write code to transform the data and use BigQuery’s API or client libraries to load it into the appropriate tables. Method 1: Webhook to BigQuery using LIKE.TG Data LIKE.TG is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources (40+ free sources), we help you not only export data from sources load data to the destinations but also transform enrich your data, make it analysis-ready. LIKE.TG Data lets you load real-time streaming data from Webhook to BigQuery in two simple steps: Step 1: Configure your source Connect LIKE.TG Data with your source, in this case, Webhooks. You also need to specify some details, such as the Event Name Path and Fields Path. Step 2: Select your Destination Load data from Webhooks to BigQuery by selecting your destination. You can also choose the options for auto-mapping and JSON fields replication here. Now you have successfully established the connection between Webhooks and BigQuery for streaming real-time data. Click here to learn more on how to Set Up Webhook as a Source. Click here to learn more on how to Set Up BigQuery as a Destination. Integrate Webhooks to BigQueryGet a DemoTry itIntegrate Webhooks to RedshiftGet a DemoTry itIntegrate Webhooks to SnowflakeGet a DemoTry it Method 2: Webhook to BigQuery ETL Using Custom Code The steps involved in migrating data from WebHook to BigQuery are as follows: Getting data out of your application using Webhook Preparing Data received from Webhook Loading data into Google BigQuery Step 1: Getting data out of your application using Webhook Setup a webhook for your application and define the endpoint URL on which you will deliver the data. This is the same URL from which the target application will read the data. Step 2: Preparing Data received from Webhook Webhooks post data to your specified endpoints in JSON format. It is up to you to parse the JSON objects and determine how to load them into your BigQuery data warehouse. You need to ensure the target BigQuery table is well aligned with the source data layout, specifically column sequence and data type of columns. Step 3: Loading data into Google BigQuery We can load data into BigQuery directly using API call or can create CSV file and then load into BigQuery table. Create a Python script to read data from the Webhook URL endpoint and load it into the BigQuery table. from google.cloud import bigquery import requests client = bigquery.Client() dataset_id = 'dataset_name' #replace with your dataset ID table_id = 'table_name' #replace with your table ID table_ref = client.dataset(dataset_id).table(table_id) table = client.get_table(table_ref) # API request receive data from WebHook Convert received data into rows to insert into BigQuery errors = client.insert_rows(table, rows_to_insert)# API request assert errors == [] You can store streaming data into a file by a specific interval and use the bq command-line tool to upload the files to your datasets, adding schema and data type information. In the GCP documentation of the GSUTIL tool, you can find the syntax of the bq command line. Iterate through this process as many times as it takes to load all of your tables into BigQuery. Once the data has been extracted from your application using Webhook, the next step is to upload it to the GCS. There are multiple techniques to upload data to GCS. Upload file to GCS bucket Using Gsutil: Using Gsutil utility we can upload a local file to GCS(Google Cloud Storage) bucket. gsutil cp local_folder/file_name.csv gs://gcs_bucket_name/path/to/folder/ To copy a file to GCS: Using Web console: An alternative way to upload the data from your local machine to GCS is using the web console. To use the web console option follow the below steps. First of all, you need to login to your GCP account. You must have a working Google account of GCP. In the menu option, click on storage and navigate to the browser on the left tab. If needed create a bucket to upload your data. Make sure that the name of the bucket you choose is globally unique. Click on the bucket name that you have created in step #2, this will ask you to browse the file from your local machine. Choose the file and click on the upload button. A progression bar will appear. Next, wait for the upload to complete. You can see the file is loaded in the bucket. Create Table in BigQuery Go to the BigQuery from the menu option. On G-Cloud console, click on create a dataset option. Next, provide a dataset name and location. Next, click on the name of the created dataset. On G-Cloud console, click on create table option and provide the dataset name, table name, project name, and table type. Load the data into BigQuery Table Once the table is created successfully, you will get a notification that will allow you to use the table as your new dataset. Alternatively, the same can be done using the Command Line as well. Start the command-line tool and click on the cloud shell icon shown here. The syntax of the bq command line to load the file in the BigQuery table: Note: The Autodetect flag identifies the table schema bq --location=[LOCATION] load --source_format=[FORMAT] [DATASET].[TABLE] [PATH_TO_SOURCE] [SCHEMA] [LOCATION] is an optional parameter that represents Location name like “us-east” [FORMAT] to load CSV file set it to CSV [DATASET] dataset name. [TABLE] table name to load the data. [PATH_TO_SOURCE] path to source file present on the GCS bucket. [SCHEMA] Specify the schema bq --location=US load --source_format=CSV your_dataset.your_table gs://your_bucket/your_data.csv ./your_schema.json You can specify your schema using bq command line Loading Schema Using the Web Console BigQuery will display all the distinct columns that were found under the Schema tab. Alternatively, to do the same in the command line, use the below command: bq --location=US load --source_format=CSV your_dataset.your_table gs://your_bucket/your_data.csv ./your_schema.json Your target table schema can also be autodetected: bq --location=US load --autodetect --source_format=CSV your_dataset.your_table gs://mybucket/data.csv BigQuery command-line interface allows us to 3 options to write to an existing table. The Web Console has the Query Editor which can be used for interacting with existing tables using SQL commands. Overwrite the table bq --location = US load --autodetect --replace --source_file_format = CSV your_target_dataset_name.your_target_table_name gs://source_bucket_name/path/to/file/source_file_name.csv Append data to the table bq --location = US load --autodetect --noreplace --source_file_format = CSV your_target_dataset_name.your_table_table_name gs://source_bucket_name/path/to/file/source_file_name.csv ./schema_file.json Adding new fields in the target table bq --location = US load --noreplace --schema_update_option = ALLOW_FIELD_ADDITION --source_file_format = CSV your_target_dataset.your_target_table gs://bucket_name/source_data.csv ./target_schema.json Update data into BigQuery Table The data that was matched in the above-mentioned steps not done complete data updates on the target table. The data is stored in an intermediate data table. This is because GCS is a staging area for BigQuery upload. There are two ways of updating the target table as described here. Update the rows in the target table. Next, insert new rows from the intermediate table UPDATE target_table t SET t.value = s.value FROM intermediate_table s WHERE t.id = s.id; INSERT target_table (id, value) SELECT id, value FROM intermediate_table WHERE NOT id IN (SELECT id FROM target_table); Delete all the rows from the target table which are in the intermediate table. Then, insert all the rows newly loaded in the intermediate table. Here the intermediate table will be in truncate and load mode. DELETE FROM final_table f WHERE f.id IN (SELECT id from intermediate_table); INSERT data_setname.target_table(id, value) SELECT id, value FROM data_set_name.intermediate_table; Sync your Webhook data to BigQuery Start for Free Now Limitations of writing custom Scripts to stream data from Webhook to BigQuery The above code is built based on a certain defined schema from the Webhook source. There are possibilities that the scripts break if the source schema is modified. If in future you identify some data transformations need to be applied on your incoming webhook events, you would require to invest additional time and resources on it. Overload of incoming data, you might have to throttle the data moving to BQ. Given you are dealing with real-time streaming data you would need to build very strong alerts and notification systems to avoid data loss due to an anomaly at the source or destination end. Since webhooks are triggered by certain events, this data loss can be very grave for your business. Webhook to BigQuery: Use Cases Inventory Management in E-commerce: E-commerce platforms can benefit from real-time inventory updates by streaming data from inventory management webhooks into BigQuery. This enables businesses to monitor stock levels, optimize supply chains, and prevent stockouts or overstocking, ensuring a seamless customer experience. Source Patient Monitoring in Healthcare: Healthcare providers can leverage real-time data streaming for patient monitoring. By connecting medical device webhooks to BigQuery, clinicians can track patient health in real time, and receive alerts for abnormal readings, and provide timely interventions, ultimately leading to better patient outcomes. Fraud Detection in Finance: Financial institutions can use webhooks to stream transaction data into BigQuery for fraud detection. Analyzing transaction patterns in real time helps to identify and prevent fraudulent activities, protect customer accounts, and ensure regulatory compliance. Event-driven marketing: Businesses across various industries can stream event data, such as user sign-ups or product launches, into BigQuery. This allows for real-time analysis of marketing campaigns, enabling quick adjustments and targeted follow-ups to boost conversion rates. Additonal Reads: Python Webhook Integration: 3 Easy Steps WhatsApp Webhook Integration: 6 Easy Steps Best Webhooks Testing tools for 2024 Conclusion In this blog, you learned two methods for streaming real-time data from Webhook to BigQuery: using an automated pipeline or writing custom ETL codes. Regarding moving data in real-time, a no-code data pipeline tool such as LIKE.TG Data can be the right choice for you. Using LIKE.TG Data, you can connect to a source of your choice and load your data to a destination of your choice cost-effectively. LIKE.TG ensures your data is reliably and securely moved from any source to BigQuery in real time. Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite firsthand. Check out our LIKE.TG Pricing to choose the best plan for you. Do let us know if the methods were helpful and if you would recommend any other methods in the comments below.
 Amazon Redshift vs Aurora: 9 Critical Differences
Amazon Redshift vs Aurora: 9 Critical Differences
AuroraDB is a relational database engine that comes as one of the options in the AWS Relational Database as a service. Amazon Redshift, on the other hand, is another completely managed database service from Amazon that can scale up to petabytes of data. Even though the ultimate aim of both these services is to let customers store and query data without getting involved in the infrastructure aspect, these two services are different in a number of ways. In this post, we will explore Amazon Redshift Vs Aurora – how these two databases compare with each other in the case of various elements and which one would be the ideal choice in different kinds of use cases. In the end, you will be in the position to choose the best platform based on your business requirements. Let’s get started. Introduction to Amazon Redshift Redshift is a completely managed database service that follows a columnar data storage structure. Redshift offers ultra-fast querying performance over millions of rows and is tailor-made for complex queries over petabytes of data. Redshift’s querying language is similar to Postgres with a smaller set of datatype collection. With Redshift, customers can choose from multiple types of instances that are optimized for performance and storage. Redshift can scale automatically in a matter of minutes in the case of the newer generation nodes. Automatic scaling is achieved by adding more nodes. A cluster can only be created using the same kind of nodes. All the administrative duties are automated with little intervention from the customer needed. You can read more on Redshift Architecture here. Redshift uses a multi-node architecture with one of the nodes being designated as a leader node. The leader node handles client communication, assigning work to other nodes, query planning, and query optimization. Redshift’s pricing combines storage and computing with the customers and does not have the pure serverless capability. Redshift offers a unique feature called Redshift spectrum which basically allows the customers to use the computing power of the Redshift cluster on data stored in S3 by creating external tables. To know more about Amazon Redshift, visit this link. Introduction to Amazon Aurora AuroraDB is a MySQL and Postgres compatible database engine; which means if you are an organization that uses either of these database engines, you can port your database to Aurora without changing a line of code. Aurora is enterprise-grade when it comes to performance and availability. All the traditional database administration tasks like hardware provisioning, backing up data, installing updates, and the likes are completely automated. Aurora can scale up to a maximum of 64 TB. It offers replication across multiple availability zones through what Amazon calls multiAZ deployment. Customers can choose from multiple types of hardware specifications for their instances depending on the use cases. Aurora also offers a serverless feature that enables a completely on-demand experience where the database will scale down automatically in case of lower loads and vice-versa. In this mode, customers only need to pay for the time the database is active, but it comes at the cost of a slight delay in response to requests that comes during the time database is completely scaled down. Amazon offers a replication feature through its multiAZ deployment strategy. This means your data is going to be replicated across multiple regions automatically and in case of a problem with your master instance, Amazon will switch to one among the replicas without affecting any loads. Aurora architecture works on the basis of a cluster volume that manages the data for all the database instances in that particular cluster. A cluster volume spans across multiple availability zones and is effectively virtual database storage. The underlying storage volume is on top of multiple cluster nodes which are distributed across different availability zones. Separate from this, the Aurora database can also have read-replicas. Only one instance usually serves as the primary instance and it supports reads as well as writes. The rest of the instances serve as read-replicas and load balancing needs to be handled by the user. This is different from the multiAZ deployment, where instances are located across the availability zone and support automatic failover. To know more about Amazon Aurora, visit this link. Introduction to OLAP and OLTP The term OLAP stands for Online Analytical Processing. OLAP analyses business data on a multidimensional level and allows for complicated computations, trend analysis, and advanced data modeling. Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting are all built on top of it. End-users may utilize OLAP to do ad hoc analysis of data in many dimensions, giving them the knowledge and information they need to make better decisions. Online Transaction Processing, or OLTP, is a form of data processing that involves completing several transactions concurrently, for example, online banking, shopping, order entry, or sending text messages. Traditionally, these transactions have been referred to as economic or financial transactions, and they are documented and secured so that an organization may access the information at any time for accounting or reporting reasons. To know more about OLAP and OLTP, visit this link. Simplify Data Analysis using LIKE.TG ’s No-code Data Pipeline LIKE.TG Data helps you directly transfer data from 150+ data sources (including 30+ free sources) to Business Intelligence tools, Data Warehouses, or a destination of your choice in a completely hassle-free automated manner. LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. It helps transfer data from a source of your choice to a destination of your choice forfree. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. LIKE.TG takes care of all your data preprocessing needs required to set up the integration and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. Get Started with LIKE.TG for Free Check out what makes LIKE.TG amazing: Real-Time Data Transfer: LIKE.TG with its strong Integration with 150+ Sources (including 30+ Free Sources), allows you to transfer data quickly efficiently. This ensures efficient utilization of bandwidth on both ends. Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer. Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss. Tremendous Connector Availability: LIKE.TG houses a large variety of connectors and lets you bring in data from numerous Marketing SaaS applications, databases, etc. such as HubSpot, Marketo, MongoDB, Oracle, Salesforce, Redshift, etc. in an integrated and analysis-ready form. Simplicity: Using LIKE.TG is easy and intuitive, ensuring that your data is exported in just a few clicks. Completely Managed Platform: LIKE.TG is fully managed. You need not invest time and effort to maintain or monitor the infrastructure involved in executing codes. Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls. Sign up here for a 14-Day Free Trial! Factors that Drive Redshift Vs Aurora Decision Both Redshift and Aurora are popular database services in the market. There is no one-size-fits-all answer here, instead, you must choose based on your company’s needs, budget, and other factors to make a Redshift vs Aurora decision. The primary factors that influence the Redshift vs Aurora comparison are as follows: Redshift vs Aurora: Scaling Redshift offer scaling by adding more nodes or upgrading the nodes. Redshift scaling can be done automatically, but the downtime in the case of Redshift is more than that of Aurora. Redshift’s concurrency scaling feature deserves a mention here. This feature is priced separately and allows a virtually unlimited number of concurrent users with the same performance if the budget is not a problem. Aurora enables scaling vertically or horizontally. Vertical scaling is through upgrading instance types and in the case of multiAZ deployment, there is minimal downtime associated with this. Otherwise, the scaling can be scheduled during the maintenance time window of the database. Aurora horizontal scaling is through read-replicas and an aurora database can have at most 15 read-replicas at the same time. Aurora compute scaling is different from storage scaling and what we mentioned above is only about compute scaling. Aurora storage scaling is done by changing the maximum allocated storage space or storage hardware type like SSD or HDD. Download the Whitepaper on Database vs Data Warehouse Learn how a Data Warehouse is different from a Database and which one should you prefer for your use case. Redshift vs Aurora: Storage Capacity Redshift can practically scale to petabytes of data and run complex queries out of them. Redshift can support up to 60 user-defined databases per cluster. Aurora, on the other hand, has a hard limit of 64 TB and the number of database instances is limited at 40. Redshift vs Aurora: Data Loading Redshift ETL also supports the COPY command for inserting data. It is recommended to insert data split into similar-sized chunks for better performance. In the case of data already existing in Redshift, you may need to use temporary tables since Redshift does not ensure unique key constraints. A detailed account of how to do ETL on Redshift can be found here. Data loading in Aurora will depend on the type of instance type that is being used. In the case of MySQL compatible instances, you would need to use the mysqlimport command or LOAD DATA IN FILE command depending on whether the data is from a MySQL table or file. Aurora with Postgres can load data with the COPY command. An alternative to this custom script-based ETL is to use a hassle-free Data Pipeline Platform like LIKE.TG which can offer a very smooth experience implementing ETL on Redshift or Aurora with support for real-time data sync, in-flight data transformations, and much more. Redshift vs Aurora: Data Structure Aurora follows row-oriented storage and supports the complete data types in both MySQL and Postgres instance types. Aurora is also an ACID complaint. Redshift uses a columnar storage structure and is optimized for column level processing than complete row level processing. Redshift’s Postgres-like querying layer misses out on many data types which are supported by Aurora’s Postgres instance type. Redshift does not support consistency among the ACID properties and only exhibits eventual consistency. It does not ensure referential integrity and unique key constraints. Redshift vs Aurora: Performance Redshift offers fast read performance and over a larger amount of data when compared to Aurora. Redshift excels specifically in the case of complicated queries spanning millions of rows. Aurora offers better performance than a traditional MySQL or Postgres instance. Aurora’s architecture disables the InnoDB change buffer for distributed storage leading to poor performance in the case of write-heavy operations. If your use case updates are heavy, it may be sensible to use traditional databases like MySQL or Postgres than Aurora. Both the services offer performance optimizations using sharding and key distribution mechanisms. Redshift’s SORT KEY and DIST KEY need to be configured here for improvements in complex queries involving JOINs. Aurora is optimized for OLTP workloads and Redshift is preferred in the case of OLAP workloads. Transactional workloads are not recommended in Redshift since it supports only eventual consistency. Redshift vs Aurora: Security When it comes to Security, there is nothing much to differentiate between the two services. With both being part of the AWS portfolio, they offer the complete set of security requirements and compliance. Data is ensured to be encrypted at rest and motion. There are provisions to establish virtual private clouds and restrict usage based on Amazon’s Identity and Access management. Other than these, customers can also use the specific security features that are part of Postgres and MySQL instance types with Aurora. Redshift vs Aurora: Maintenance Both Aurora and Redshift are completely managed services and required very little maintenance. Redshift because of its delete marker-based architecture needs the VACUUM command to be executed periodically to reclaim the space after entries are deleted. These can be scheduled periodically, but it is a recommended practice to execute this command in case of heavy updates and delete workload. Redshift also needs the ANALYZE command to be executed periodically to keep the metadata up to data for query planning. Redshift vs Aurora: Pricing Redshift pricing is including storage and compute power. Redshift starts at .25$ per hour for the dense compute instance types per node. Dense compute is the recommended instance type for up to 500 GB of data. For the higher-spec dense storage instance types, pricing starts at .85$. It is to be noted that these two services are designed for different use cases and pricing can not be compared independent of the customer use cases. Aurora MySQL starts with .041$ per hour for its lowest spec instance type. Aurora Postgres starts at .082$ per hour for the same type of instance. The memory-optimized instance types with higher performance start for .29$ for both MySQL and Postgres instance types. Aurora’s serverless instances are charged based on ACU hours and start at .06$ per ACU hour. Storage and IO are charged separately for Aurora. It costs .1 $ per GB per month and .2$ per 1 million requests. Aurora storage pricing is based on the maximum storage ever used by the cluster and it is not possible to reclaim space after being deleted without re instantiating the database. An obvious question after such a long comparison is about how to decide when to use Redshift and Aurora for your requirement. The following section summarizes the scenarios in which using one of them may be beneficial over the other. Redshift vs Aurora: Use Cases Use Cases of Amazon Redshift The requirement is an Online analytical processing workload and not transactional. You have a high analytical workload and running on your transactional database will hurt the performance. Your data volume is in hundreds of TBs and you anticipate more data coming in. You are willing to let go of the consistency compliance and will ensure the uniqueness of your keys on your own. You are ready to put your head into designing SORT KEYS and DIST KEYS to extract the maximum performance. Use Cases of Amazon Aurora You want to relieve yourself of the administrative tasks of managing a database but want to stick with MySQL or Postgres compatible querying layer. You want to stay with traditional databases like MySQL or Postgres but want better read performance at the cost of slightly lower write and update performance. Your storage requirements are only in the TBs and do not anticipate 100s of TBs of data in the near future. You have an online transactional processing use case and want quick results with a smaller amount of data. Your OLTP workloads are not interrupted by analytical workloads Your analytical workloads do not need to process millions of rows of data. Conclusion This article gave a comprehensive guide on difference between Aurora vs Redshift. You got a deeper understanding of Redshift and Aurora. Now, you are in the position to choose the best among the two based on your business goals and requirements. To conclude, the Redshift vs Aurora decision is entirely based on the company’s goals, resources, and also a matter of personal preference. Visit our Website to Explore LIKE.TG Businesses can use automated platforms like LIKE.TG Data to set the integration and handle the ETL process. It helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you a hassle-free experience. It helps transfer data from a source of your choice to a destination of your choice forfree. Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand. You can also have a look at the unbeatable LIKE.TG Pricing that will help you choose the right plan for your business needs. What use case are you evaluating these platforms for? Let us know in the comments. We would be happy to help solve your dilemma.
 MS SQL Server to Redshift: 3 Easy Methods
MS SQL Server to Redshift: 3 Easy Methods
With growing volumes of data, is your SQL Server getting slow for analytical queries? Are you simply migrating data from MS SQL Server to Redshift? Whatever your use case, we appreciate your smart move to transfer data from MS SQL Server to Redshift. This article, in detail, covers the various approaches you could use to load data from SQL Server to Redshift. This article covers the steps involved in writing custom code to load data from SQL Server to Amazon Redshift. Towards the end, the blog also covers the limitations of this approach. Note: For MS SQL to Redshift migrations, compatibility and performance optimization for the transferred SQL Server workloads must be ensured. What is MS SQL Server? Microsoft SQL Server is a relational database management system (RDBMS) developed by Microsoft. It is designed to store and retrieve data as requested by other software applications, which can run on the same computer or connect to the database server over a network. Some key features of MS SQL Server: It is primarily used for online transaction processing (OLTP) workloads, which involve frequent database updates and queries. It supports a variety of programming languages, including T-SQL (Transact-SQL), .NET languages, Python, R, and more. It provides features for data warehousing, business intelligence, analytics, and reporting through tools like SQL Server Analysis Services (SSAS), SQL Server Integration Services (SSIS), and SQL Server Reporting Services (SSRS). It offers high availability and disaster recovery features like failover clustering, database mirroring, and log shipping. It supports a wide range of data types, including XML, spatial data, and in-memory tables. What is Amazon Redshift? Amazon Redshift is a cloud-based data warehouse service offered by Amazon Web Services (AWS). It’s designed to handle massive amounts of data, allowing you to analyze and gain insights from it efficiently. Here’s a breakdown of its key features: Scalability:Redshift can store petabytes of data and scale to meet your needs. Performance:It uses a parallel processing architecture to analyze large datasets quickly. Cost-effective:Redshift offers pay-as-you-go pricing, so you only pay for what you use. Security:Built-in security features keep your data safe. Ease of use:A fully managed service, Redshift requires minimal configuration. Understanding the Methods to Connect SQL Server to Redshift A good understanding of the different Methods to Migrate SQL Server To Redshift can help you make an informed decision on the suitable choice. These are the three methods you can implement to set up a connection from SQL Server to Redshift in a seamless fashion: Method 1: Using LIKE.TG Data to Connect SQL Server to Redshift Method 2: Using Custom ETL Scripts to Connect SQL Server to Redshift Method 3: Using AWS Database Migration Service (DMS) to Connect SQL Server to Redshift Method 1: Using LIKE.TG Data to Connect SQL Server to Redshift LIKE.TG helps you directly transfer data from SQL Server and various other sources to a Data Warehouse, such as Redshift, or a destination of your choice in a completely hassle-free automated manner. LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled securely and consistently with zero data loss. Sign up here for a 14-Day Free Trial! LIKE.TG takes care of all your data preprocessing to set up SQL Server Redshift migration and lets you focus on key business activities and draw a much more powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. Step 1: Configure MS SQL Server as your Source ClickPIPELINESin theNavigation Bar. Click+ CREATEin thePipelines List View. In theSelect Source Typepage, select theSQLServer variant In theConfigure yourSQL Server Sourcepage, specify the following: Step 2: Select the Replication Mode Select the replication mode: (a) Full Dump and Load (b) Incremental load for append-only data (c) Incremental load for mutable data. Step 3: Integrate Data into Redshift ClickDESTINATIONSin theNavigation Bar. Click+ CREATEin theDestinations List View. In theAdd Destinationpage, selectAmazonRedshift. In theConfigure your AmazonRedshift Destinationpage, specify the following: As can be seen, you are simply required to enter the corresponding credentials to implement this fully automated data pipeline without using any code. Check out what makes LIKE.TG amazing: Real-Time Data Transfer: LIKE.TG with its strong Integration with 100+ sources, allows you to transfer data quickly efficiently. This ensures efficient utilization of bandwidth on both ends. Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer. Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled securely and consistently with zero data loss. Tremendous Connector Availability: LIKE.TG houses a large variety of connectors and lets you bring in data from numerous Marketing SaaS applications, databases, etc. such as Google Analytics 4, Google Firebase, Airflow, HubSpot, Marketo, MongoDB, Oracle, Salesforce, Redshift, etc. in an integrated and analysis-ready form. Simplicity: Using LIKE.TG is easy and intuitive, ensuring that your data is exported in just a few clicks. Completely Managed Platform: LIKE.TG is fully managed. You need not invest time and effort to maintain or monitor the infrastructure involved in executing codes. Get Started with LIKE.TG for Free Method 2: Using Custom ETL Scripts to Connect SQL Server to Redshift As a pre-requisite to this process, you will need to have installed Microsoft BCP command-line utility. If you have not installed it, here is the link to download it. For demonstration, let us assume that we need to move the ‘orders’ table from the ‘sales’ schema into Redshift. This table is populated with the customer orders that are placed daily. There might be two cases you will consider while transferring data. Move data for one time into Redshift. Incrementally load data into Redshift. (when the data volume is high) Let us look at both scenarios: One Time Load You will need to generate the .txt file of the required SQL server table using the BCP command as follows : Open the command prompt and go to the below path to run the BCP command C:Program Files <x86>Microsoft SQL ServerClient SDKODBC130ToolsBinn Run BCP command to generate the output file of the SQL server table Sales bcp "sales.orders" out D:outorders.txt -S "ServerName" -d Demo -U UserName -P Password -c Note: There might be several transformations required before you load this data into Redshift. Achieving this using code will become extremely hard. A tool like LIKE.TG , which provides an easy environment to write transformations, might be the right thing for you. Here are the steps you can use in this step: Step 1: Upload Generated Text File to S3 Bucket Step 2: Create Table Schema Step 3: Load the Data from S3 to Redshift Using the Copy Command Step 1: Upload Generated Text File to S3 Bucket We can upload files from local machines to AWS using several ways. One simple way is to upload it using the file upload utility of S3. This is a more intuitive alternative.You can also achieve this AWS CLI, which provides easy commands to upload it to the S3 bucket from the local machine.As a pre-requisite, you will need to install and configure AWS CLI if you have not already installed and configured it. You can refer to the user guide to know more about installing AWS CLI.Run the following command to upload the file into S3 from the local machine aws s3 cp D:orders.txt s3://s3bucket011/orders.txt Step 2: Create Table Schema CREATE TABLE sales.orders (order_id INT, customer_id INT, order_status int, order_date DATE, required_date DATE, shipped_date DATE, store_id INT, staff_id INT ) After running the above query, a table structure will be created within Redshift with no records in it. To check this, run the following query: Select * from sales.orders Step 3: Load the Data from S3 to Redshift Using the Copy Command COPY dev.sales.orders FROM 's3://s3bucket011/orders.txt' iam_role 'Role_ARN' delimiter 't'; You will need to confirm if the data has loaded successfully. You can do that by running the query. Select count(*) from sales.orders This should return the total number of records inserted. Limitations of using Custom ETL Scripts to Connect SQL Server to Redshift In cases where data needs to be moved once or in batches only, the custom ETL script method works well. This approach becomes extremely tedious if you have to copy data from MS SQL to Redshift in real-time. In case you are dealing with huge amounts of data, you will need to perform incremental load. Incremental load (change data capture) becomes hard as there are additional steps that you need to follow to achieve it. Transforming data before you load it into Redshift will be extremely hard to achieve. When you write code to extract a subset of data often those scripts break as the source schema keeps changing or evolving. This can result in data loss. The process mentioned above is frail, erroneous, and often hard to implement and maintain. This will impact the consistency and availability of your data into Amazon Redshift. Download the Cheatsheet on How to Set Up High-performance ETL to Redshift Learn the best practices and considerations for setting up high-performance ETL to Redshift Method 3: Using AWS Database Migration Service (DMS) AWS Database Migration Service (DMS) offers a seamless pathway for transferring data between databases, making it an ideal choice for moving data from SQL Server to Redshift. This fully managed service is designed to minimize downtime and can handle large-scale migrations with ease. For those looking to implement SQL Server CDC (Change Data Capture) for real-time data replication, we provide a comprehensive guide that delves into the specifics of setting up and managing CDC within the context of AWS DMS migrations. Detailed Steps for Migration: Setting Up a Replication Instance: The first step involves creating a replication instance within AWS DMS. This instance acts as the intermediary, facilitating the transfer of data by reading from SQL Server, transforming the data as needed, and loading it into Redshift. Creating Source and Target Endpoints: After the replication instance is operational, you’ll need to define the source and target endpoints. These endpoints act as the connection points for your SQL Server source database and your Redshift target database. Configuring Replication Settings: AWS DMS offers a variety of settings to customize the replication process. These settings are crucial for tailoring the migration to fit the unique needs of your databases and ensuring a smooth transition. Initiating the Replication Process: With the replication instance and endpoints in place, and settings configured, you can begin the replication process. AWS DMS will start the data transfer, moving your information from SQL Server to Redshift. Monitoring the Migration: It’s essential to keep an eye on the migration as it progresses. AWS DMS provides tools like CloudWatch logs and metrics to help you track the process and address any issues promptly. Verifying Data Integrity: Once the migration concludes, it’s important to verify the integrity of the data. Conducting thorough testing ensures that all data has been transferred correctly and is functioning as expected within Redshift. The duration of the migration is dependent on the size of the dataset but is generally completed within a few hours to days. The sql server to redshift migration process is often facilitated by AWS DMS, which simplifies the transfer of database objects and data For a step-by-step guide, please refer to the official AWS documentation. Limitations of Using DMS: Not all SQL Server features are supported by DMS. Notably, features like SQL Server Agent jobs, CDC, FILESTREAM, and Full-Text Search are not available when using this service. The initial setup and configuration of DMS can be complex, especially for migrations that involve multiple source and target endpoints. Conclusion That’s it! You are all set. LIKE.TG will take care of fetching your data incrementally and will upload that seamlessly from MS SQL Server to Redshift via a real-time data pipeline. Extracting complex data from a diverse set of data sources can be a challenging task and this is where LIKE.TG saves the day! Visit our Website to Explore LIKE.TG LIKE.TG offers a faster way to move data from Databases or SaaS applications like SQL Server into your Data Warehouse like Redshift to be visualized in a BI tool. LIKE.TG is fully automated and hence does not require you to code. Sign Up for a 14-day free trial to try LIKE.TG for free. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs. Tell us in the comments about data migration from SQL Server to Redshift!
 Snowflake Architecture & Concepts: A Comprehensive Guide
Snowflake Architecture & Concepts: A Comprehensive Guide
This article helps focuses on an in-depth understanding of Snowflake architecture, how it stores and manages data, and its micro-partitioning concepts. By the end of this blog, you will also be able to understand how Snowflake architecture is different from the rest of the cloud-based Massively Parallel Processing Databases.What is a Data Warehouse? Businesses today are overflowing with data. The amount of data produced every day is truly staggering. With Data Explosion, it has become seemingly difficult to capture, process, and store big or complex datasets. Hence, it becomes a necessity for organizations to have a Central Repository where all the data is stored securely and can be further analyzed to make informed decisions. This is where Data Warehouses come into the picture. A Data Warehouse also referred to as “Single Source of Truth”, is a Central Repository of information that supports Data Analytics and Business Intelligence (BI) activities. Data Warehouses store large amounts of data from multiple sources in a single place and are intended to execute queries and perform analysis for optimizing their business. Its analytical capabilities allow organizations to derive valuable business insights from their data to improve decision-making. What is the Snowflake Data Warehouse? Snowflake is a cloud-based Data Warehouse solution provided as a Saas (Software-as-a-Service) with full support for ANSI SQL. It also has a unique architecture that enables users to just create tables and start querying data with very less administration or DBA activities needed. Know about Snowflake pricing here. Download the Cheatsheet on How to Set Up ETL to Snowflake Learn the best practices and considerations for setting up high-performance ETL to Snowflake Features of Snowflake Data Warehouse Let’s discuss some major features of Snowflake data warehouse: Security and Data Protection: Snowflake data warehouse offers enhanced authentication by providing Multi-Factor Authentication (MFA), federal authentication and Single Sign-on (SSO) and OAuth. All the communication between the client and server is protected y TLS. Standard and Extended SQL Support: Snowflake data warehouse supports most DDL and DML commands of SQL. It also supports advanced DML, transactions, lateral views, stored procedures, etc. Connectivity: Snowflake data warehouse supports an extensive set of client connectors and drivers such as Python connector, Spark connector, Node.js driver, .NET driver, etc. Data Sharing: You can securely share your data with other Snowflake accounts. Read more about the features of Snowflake data warehouse here. Let’s learn about Snowflake architecture in detail. LIKE.TG Data: A Convenient Method to Explore your Snowflake Data LIKE.TG is a No-code Data Pipeline. It supports pre-built data integration from100+ data sourcesat a reasonableprice. It can automate your entire data migration process in minutes. It offers a set of features and supports compatibility with several databases and data warehouses. Get Started with LIKE.TG for Free Let’s see some unbeatable features of LIKE.TG : Simple:LIKE.TG has a simple and intuitive user interface. Fault-Tolerant:LIKE.TG offers a fault-tolerant architecture. It can automatically detect anomalies and notifies you instantly. If there is any affected record, then it is set aside for correction. Real-Time:LIKE.TG has a real-time streaming structure, which ensures that your data is always ready for analysis. Schema Mapping:LIKE.TG will automatically detect schema from your incoming data and maps it to your destination schema. Data Transformation:It provides a simple interface to perfect, modify, and enrich the data you want to transfer. Live Support:LIKE.TG team is available round the clock to extend exceptional support to you through chat, email, and support call. Sign up here for a 14-Day Free Trial! Types of Data Warehouse Architecture There are mainly 3 ways of developing a Data Warehouse: Single-tier Architecture: This type of architecture aims to deduplicate data in order to minimize the amount of stored data. Two-tier Architecture: This type of architecture aims to separate physical Data Sources from the Data Warehouse. This makes the Data Warehouse incapable of expanding and supporting multiple end-users. Three-tier Architecture: This type of architecture has 3 tiers in it. The bottom tier consists of the Database of the Data Warehouse Servers, the middle tier is an Online Analytical Processing (OLAP) Server used to provide an abstracted view of the Database, and finally, the top tier is a Front-end Client Layer consisting of the tools and APIs used for extracting data. Components of Data Warehouse Architecture The 4 components of a Data Warehouse are as follows. 1. Data Warehouse Database A Database forms an essential component ofa Data Warehouse. A Database stores and provides access to company data. Amazon Redshift and Azure SQL come under Cloud-based Database services. 2. Extraction, Transformation, and Loading Tools (ETL) All the operations associated with the Extraction, Transformation, and Loading (ETL) of data into the warehouse come under this component. Traditional ETL tools are used to extract data from multiple sources, transform it into a digestible format, and finally load it into a Data Warehouse. 3. Metadata Metadata provides a framework and descriptions of data, enabling the construction, storage, handling, and use of the data. 4. Data Warehouse Access Tools Access Tools allow users to access actionable and business-ready information froma Data Warehouse. TheseWarehouse Toolsinclude Data Reporting tools, Data Querying Tools, Application Development tools, Data Mining tools, and OLAP tools. Snowflake Architecture Snowflake architecture comprises a hybrid of traditional shared-disk and shared-nothing architectures to offer the best of both. Let us walk through these architectures and see how Snowflake combines them into new hybrid architecture. Overview of Shared-Disk Architecture Overview of Shared-Nothing Architecture Snowflake Architecture – A Hybrid Model Storage Layer Compute Layer Cloud Services Layer Overview of Shared-Disk Architecture Used in traditional databases, shared-disk architecture has one storage layer accessible by all cluster nodes. Multiple cluster nodes having CPU and Memory with no disk storage for themselves communicate with central storage layer to get the data and process it. Overview of Shared-Nothing Architecture Contrary to Shared-Disk architecture, Shared-Nothing architecture has distributed cluster nodes along with disk storage, their own CPU, and Memory. The advantage here is that the data can be partitioned and stored across these cluster nodes as each cluster node has its own disk storage. Snowflake Architecture – A Hybrid Model Snowflake supports a high-level architecture as depicted in the diagram below. Snowflake has 3 different layers: Storage Layer Compute Layer Cloud Services Layer 1. Storage Layer Snowflake organizes the data into multiple micro partitions that are internally optimized and compressed. It uses a columnar format to store. Data is stored in the cloud storage and works as a shared-disk model thereby providing simplicity in data management. This makes sure users do not have to worry about data distribution across multiple nodes in the shared-nothing model. Compute nodes connect with storage layer to fetch the data for query processing. As the storage layer is independent, we only pay for the average monthly storage used. Since Snowflake is provisioned on the Cloud, storage is elastic and is charged as per the usage per TB every month. 2. Compute Layer Snowflake uses “Virtual Warehouse” (explained below) for running queries. Snowflake separates the query processing layer from the disk storage. Queries execute in this layer using the data from the storage layer. Virtual Warehouses are MPP compute clusters consisting of multiple nodes with CPU and Memory provisioned on the cloud by Snowflake. Multiple Virtual Warehouses can be created in Snowflake for various requirements depending upon workloads. Each virtual warehouse can work with one storage layer. Generally, a virtual Warehouse has its own independent compute cluster and doesn’t interact with other virtual warehouses. Advantages of Virtual Warehouse Some of the advantages of virtual warehouse are listed below: Virtual Warehouses can be started or stopped at any time and also can be scaled at any time without impacting queries that are running. They also can be set to auto-suspend or auto-resume so that warehouses are suspended after a specific period of inactive time and then when a query is submitted are resumed. They can also be set to auto-scale with minimum and maximum cluster size, so for e.g. we can set minimum 1 and maximum 3 so that depending on the load Snowflake can provision between 1 to 3 multi-cluster warehouses. 3. Cloud Services Layer All the activities such as authentication, security, metadata management of the loaded data and query optimizer that coordinate across Snowflake happens in this layer. Examples of services handled in this layer: When a login request is placed it has to go through this layer, Query submitted to Snowflake will be sent to the optimizer in this layer and then forwarded to Compute Layer for query processing. Metadata required to optimize a query or to filter a data are stored in this layer. These three layers scale independently and Snowflake charges for storage and virtual warehouse separately. Services layer is handled within compute nodes provisioned, and hence not charged. The advantage of this Snowflake architecture is that we can scale any one layer independently of others. For e.g. you can scale storage layer elastically and will be charged for storage separately. Multiple virtual warehouses can be provisioned and scaled when additional resources are required for faster query processing and to optimize performance. Know more about Snowflake architecture from here. Connecting to Snowflake Now that you’re familiar with Snowflake’s architecture, it’s now time to discuss how you can connect to Snowflake. Let’s take a look at some of the best third-party tools and technologies that form the extended ecosystem for connecting to Snowflake. Snowflake Ecosystem— This list will take you through Snowflake’s partners, clients, third-party tools, and emerging technologies in the Snowflake ecosystem. Third-party partners and technologies are certified to provide native connectivity to Snowflake. Data Integration or ETL tools are known to provide native connectivity to Snowflake. Business intelligence (BI) tools simplify analyzing, discovering, and reporting on business data to help organizations make informed business decisions. Machine Learning Data Science cover a broad category of vendors, tools, and technologies that extend Snowflake’s functionality to provide advanced capabilities for statistical and predictive modeling. Security Governance tools ensure that your data is stored and maintained securely. Snowflake also provides native SQL Development and Data Querying interfaces. Snowflake supports developing applications using many popular programming languages and development platforms. Snowflake Partner Connect— This list will take you through Snowflake partners who offer free trials for connecting to Snowflake. General Configuration (All Clients)— This is a set of general configuration instructions that is applicable to all Snowflake-provided Clients (CLI, connectors, and drivers). SnowSQL (CLI Client)— SnowSQL is a next-generation command-line utility for connecting to Snowflake. It allows you to execute SQL queries and perform all DDL and DML operations. Connectors Drivers– Snowflake provides drivers and connectors for Python, JDBC, Spark, ODBC, and other clients for application development. You can go through each of them listed below to start learning and using them. Snowflake Connector for Python Snowflake Connector for Spark Snowflake Connector for Kafka Node.js Driver Go Snowflake Driver .NET Driver JDBC Driver ODBC Driver PHP PDO Driver for Snowflake You can always connect to Snowflake via the above-mentioned tools/technologies. Conclusion Ever since 2014, Snowflake has been simplifying how organizations store and interact with their data. In this blog, you have learned about Snowflake’s data warehouse, Snowflake architecture, and how it stores and manages data. You learned about various layers of the hybrid model in Snowflake architecture. Check out more articles about the Snowflake data warehouse to know about vital Snowflake data warehouse featuresand Snowflake best practices for ETL. You can have a good working knowledge of Snowflake by understandingSnowflake Create Table. Visit our Website to Explore LIKE.TG LIKE.TG , an official Snowflake ETL Partner, can help bring your data from various sources to Snowflake in real-time. You canreach out to us or take up a free trial if you need help in setting up your Snowflake Architecture or connecting your data sources to Snowflake. Give LIKE.TG a try! Sign Up here for a 14-day free trial today. If you still have any queries related to Snowflake Architecture, feel free to discuss them in the comment section below.
 How to Connect DynamoDB to S3? : 5 Easy Steps
How to Connect DynamoDB to S3? : 5 Easy Steps
Moving data from Amazon DynamoDB to S3 is one of the efficient ways to derive deeper insights from your data. If you are trying to move data into a larger database. Well, you have landed on the right article. Now, it has become easier to replicate data from DynamoDB to S3.This article will give you a brief overview of Amazon DynamoDB and Amazon S3. You will also get to know how you can set up your DynamoDB to S3 integration using 4 easy steps. Moreover, the limitations of the method will also be discussed. Read along to know more about connecting DynamoDB to S3 in the further sections. Prerequisites You will have a much easier time understanding the ways for setting up the DynamoDB to S3 integration if you have gone through the following aspects: An active AWS account.Working knowledge of the ETL process. What is Amazon DynamoDB? Amazon DynamoDB is a document and key-value Database with a millisecond response time. It is a fully managed, multi-active, multi-region, persistent Database for internet-scale applications with built-in security, in-memory cache, backup, and restore. It can handle up to 10 trillion requests per day and 20 million requests per second. Some of the top companies like Airbnb, Toyota, Samsung, Lyft, and Capital One rely on DynamoDB’s performance and scalability. Simplify Data Integration With LIKE.TG ’s No Code Data Pipeline LIKE.TG Data, an Automated No-code Data Pipeline, helps you directly transfer data fromAmazon DynamoDB,S3,and150+ other sources(50+ free sources) to Business Intelligence tools, Data Warehouses, or a destination of your choice in a completely hassle-free automated manner. LIKE.TG ’s fully managed pipeline uses DynamoDB’sdata streamsto support Change Data Capture (CDC) for its tables and ingests new information viaAmazon DynamoDB StreamsAmazon Kinesis Data Streams. LIKE.TG also enables you to load data from files in anS3 bucketinto your Destination database or Data Warehouse seamlessly. Moreover, S3 stores its files after compressing them into aGzipformat. LIKE.TG ’s Data pipeline automatically unzips anyGzipped fileson ingestion and also performs file re-ingestion in case there is any data update. Get Started with LIKE.TG for Free With LIKE.TG in place, you can automate the Data Integration process which will help in enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure and flexible manner with zero data loss. LIKE.TG ’s consistent reliable solution to manage data in real-time allows you to focus more on Data Analysis, instead of Data Consolidation. What is Amazon S3? Amazon S3 is a fully managed object storage service used for a variety of purposes like data hosting, backup and archiving, data warehousing, and much more. Through an easy-to-use control panel interface, it provides comprehensive access controls to suit any kind of organizational and commercial compliance requirements. S3 provides high availability by distributing data across multiple servers. This strategy, of course, comes with a propagation delay, however, S3 only guarantees eventual consistency. Also, in the case of Amazon S3, the API will always return either new or old data and will never provide a damaged answer. What is AWS Data Pipeline? AWS Data Pipeline is a Data Integration solution provided by Amazon. With AWS Data Pipeline, you just need to define your source and destination and AWS Data Pipeline takes care of your data movement. This will avoid your development and maintenance efforts. With the help of a Data Pipeline, you can apply pre-condition/post-condition checks, set up an alarm, schedule the pipeline, etc.This article will only focus on data transfer through the AWS Data Pipeline alone. Limitations:Per account, you can have a maximum of 100 pipelines and objects per pipeline. Steps to Connect DynamoDB to S3 using AWS Data Pipeline You can follow the below-mentioned steps to connect DynamoDB to S3 using AWS Data Pipeline: Step 1: Create an AWS Data Pipeline from the built-in template provided by Data Pipeline for data export from DynamoDB to S3 as shown in the below image. Step 2: Activate the Pipeline once done. Step 3: Once the Pipeline is finished, check whether the file is generated in the S3 bucket. Step 4: Go and download the file to see the content. Step 5: Check the content of the generated file. With this, you have successfully set up DynamoDB to S3 Integration. Advantages of exporting DynamoDB to S3 using AWS Data Pipeline AWS provides an automatic template for Dynamodb to S3 data export and very less setup is needed in the pipeline. It internally takes care of your resources i.e. EC2 instances and EMR cluster provisioning once the pipeline is activated.It provides greater resource flexibility as you can choose your instance type, EMR cluster engine, etc.This is quite handy in cases where you want to hold your baseline data or take a backup of DynamoDB table data to S3 before further testing on the DynamoDB table and can revert to the table once done with testing.Alarms and notifications can be handled beautifully using this approach. Disadvantages of exporting DynamoDB to S3 using AWS Data Pipeline The approach is a bit old-fashioned as it utilizes EC2 instances and triggers the EMR cluster to perform the export activity. If the instance and the cluster configuration are not properly provided in the pipeline, it could cost dearly. Sometimes EC2 instance or EMR cluster fails due to resource unavailability etc. This could lead to the pipeline getting failed. Even though the solutions provided by AWS work but it is not much flexible and resource optimized. These solutions either require additional AWS services or cannot be used to copy data from multiple tables across multiple regions easily. You can use LIKE.TG , an automated Data Pipeline platform for Data Integration and Replication without writing a single line of code. Using LIKE.TG , you can streamline your ETL process with its pre-built native connectors with various Databases, Data Warehouses, SaaS applications, etc. You can also check out our blog on how to move data from DynamoDB to Amazon S3 using AWS Glue. Solve your data integration problems with LIKE.TG ’s reliable, no-code, automated pipelines with 150+ connectors.Get your free trial right away! Conclusion Overall, using the AWS Data Pipeline is a costly setup, and going with serverless would be a better option. However, if you want to use engines like Hive, Pig, etc., then Pipeline would be a better option to import data from the DynamoDB table to S3. Now, the manual approach of connecting DynamoDB to S3 using AWS Glue will add complex overheads in terms of time and resources. Such a solution will require skilled engineers and regular data updates. LIKE.TG Data provides an Automated No-code Data Pipeline that empowers you to overcome the above-mentioned limitations. LIKE.TG caters to 150+ data sources (including 50+ free sources) and can seamlessly transfer your S3 and DynamoDB data to the Data Warehouse of your choice in real time. LIKE.TG ’s Data Pipeline enriches your data and manages the transfer process in a fully automated and secure manner without having to write any code. Learn more about LIKE.TG Share your experience of connecting DynamoDB to S3 in the comments section below!
 Facebook Ads to Redshift Simplified: 2 Easy Methods
Facebook Ads to Redshift Simplified: 2 Easy Methods
Your organization must be spending many dollars to market and acquire customers through Facebook Ads. Given the importance and cost-share, this medium occupies, moving all important data to a robust warehouse such as Redshift becomes a business requirement for better analysis, market insight, and growth. This post talks about moving your data from Facebook Ads to the Redshift in an efficient and reliable manner.Prerequisites An active Facebook account.An active Amazon Redshift account. Understanding Facebook Ads and Redshift Facebook is the world’s biggest online social media giant with over 2 billion users around the world, making it one of the leading advertisement channels in the world. Studies have shown that Facebook accounts for over half of the advertising spends in the US. Facebook ads target users based on multiple factors like activity, demographic information, device information, advertising, and marketing partner-supplied information, etc. Amazon Redshift is a simple, cost-effective and yet very fast and easily scalable cloud data warehouse solution capable of analyzing petabyte-level data. Redshift provides new and deeper insights into the customer response behavior, marketing, and overall business by merging and analyzing the Facebook data as well as data from other sources simultaneously. You can read more on the features of Redshift here. How to transfer data from Facebook Ads to Redshift? Data can be moved from Facebook Ads to Redshift in either of two ways: Method 1:Write custom ETL scripts to load data The manual method calls for you to write a custom ETL script yourself. So, you will have to write the script to extract the data from Facebook Ads, transform the data (i.e select and remove whatever is not needed) and then load it to Redshift. This method would you to invest a considerable amount of engineering resources Method 2:Use a fully managed Data Integration Platform likeLIKE.TG Data Using an easy-to-use Data Integration Platform like LIKE.TG helps you move data from Facebook Ads to Redshift within a couple of minutes and for free. There’s no need to write any code as LIKE.TG offers a graphical interface to move data. LIKE.TG is a fully managed solution, which means there is zero monitoring and maintenance needed from your end. Get Started with LIKE.TG for free Methods to Load Data from Facebook Ads to Redshift Majorly there are 2 methods through which you can load your data from Facebook Ads to Redshift: Method 1: Moving your data from Facebook Ads to Redshift using Custom ScriptsMethod 2: Moving your data from Facebook Ads to Redshift using LIKE.TG Method 1: Moving your data from Facebook Ads to Redshift using Custom Scripts The fundamental idea is simple – fetch the data from Facebook Ads, transform the data so that Redshift can understand it, and finally load the data into Redshift. Following are the steps involved if you chose to move data manually: To fetch the data you have to use the Facebook Ads Insight API and write scripts for it. Look into the API documentation to find out all the endpoints available and access it. These Endpoints (impressions, clickthrough rates, CPC, etc.) are broken out by time period. The endpoints will return a JSON output. Once you receive the output then you need to extract only the fields that matter to you. To get newly updated data as it appears in Facebook Ads on a regular basis, you also need to set up cron jobs. For this, you need to identify the auto-incrementing key fields that your written script can use to bookmark its progression through the dataNext, to map Facebook ad’s JSON files, you need to identify all the columns you want to insert and then set up a table in Redshift matching this schema. Next, you would have to write a script to insert this data into Redshift. Datatype compatibility between the two platforms is another area you need to be careful about. For each field in the Insights API’s response, you have to decide on the appropriate data type in the redshift table. In the case of a small amount of data, building an insert operation seems natural. However, keep in mind that Redshift is not optimized for row-by-row updates. So for large data, it is always recommended to use an intermediary like Amazon S3 (AWS) and then copy the data to Redshift. In this case, you are required to – Create a bucket for your dataWrite an HTTP PUT for your AWS REST API using Postman, Python, or Curl Once the bucket is in place, you can then send your data to S3Then use a COPY command to load data from S3 to Redshift Additionally, you need to put in place proper frequent monitoring to detect any change in the Facebook Ad schema and update the script in case of any change in the source data structure. Method 2: Moving your data from Facebook Ads to Redshift using LIKE.TG LIKE.TG Data, a No-code Data Pipeline helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. It supports 100+ data sources(including 40+ free sources) including Facebook Ads, etc.,for free and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. LIKE.TG loads the data onto the desired Data Warehouse, enriches the data, and transforms it into an analysis-ready form without writing a single line of code. Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different Business Intelligence (BI) tools as well. LIKE.TG can move data from Facebook Ads to Redshift seamlessly in 2 simple steps: Step 1: Configuring the Source Navigate to the Asset Palette and click on Pipelines.Now, click on the +CREATE button and select Facebook Ads as the source for data migration.In theConfigure your Facebook Adspage, clickon ADD FACEBOOK ADS ACCOUNT.Login to your Facebook account and click on Done to authorize LIKE.TG to access your Facebook Ads data. In theConfigure your Facebook Ads Sourcepage, fill all the required fields Step 2: Configuring the Destination Once you have configured the source, it’s time to manage the destination. navigate to the Asset Palette and click on Destination.Click on the +CREATE button and select Amazon Redshift as the destination.In theConfigure your Amazon Redshift Destinationpage, specify all the necessary details. LIKE.TG will now take care of all the heavy-weight lifting to move data from Google Ads to Redshift. Get Started with LIKE.TG for free Advantages of Using LIKE.TG Listed below are the advantages of using LIKE.TG Data over any other Data Pipeline platform: Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Schema Management: LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to the destination schema.Minimal Learning: LIKE.TG , with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.LIKE.TG Is Built To Scale: As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency.Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.Live Monitoring: LIKE.TG allows you to monitor the data flow and check where your data is at a particular point in time. Limitations of Using the Custom Code Method to Move Data On the surface, implementing a custom solution to move data from Facebook Ads to Redshift may seem like a more viable solution. However, you must be aware of the limitations of this approach as well. Since you are writing it yourself, you have to maintain it too. If Facebook updates its API or the API sends a field with a datatype which your code doesn’t recognize, then you will have to modify your script likewise. Script modification is also needed even if slightly different information is needed by users.You also need a data validation system in place to ensure all the data is being updated accurately.The process is time-consuming and you might want to put your time to better use if a better less time-consuming process is available.Though maintaining in this way is very much possible, this requires plenty of engineering resources which is not suited for today’s agile work environment. Conclusion The article introduced you to Facebook Ads and Amazon Redshift. It provided 2 methods that you can use for loading data from Facebook Ads to Redshift. The 1st method includes Manual Integration while the 2nd method uses LIKE.TG Data. Visit our Website to Explore LIKE.TG With the complexity involves in Manual Integration, businesses are leaning more towards Automated and Continous Integration. This is not only hassle-free but also easy to operate and does not require any technical proficiency. In such a case, LIKE.TG Data is the right choice for you! It will help simplify the Marketing Analysis. LIKE.TG Data supports platforms like Facebook Ads, etc., for free. Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand. You can also have a look at our unbeatablepricingthat will help you choose the right plan for your business needs! What are your thoughts on moving data from Facebook Ads to Redshift? Let us know in the comments.
 Connecting Aurora to Redshift using AWS Glue: 7 Easy Steps
Connecting Aurora to Redshift using AWS Glue: 7 Easy Steps
Are you trying to derive deeper insights from your Aurora Database by moving the data into a larger Database like Amazon Redshift? Well, you have landed on the right article. Now, it has become easier to replicate data from Aurora to Redshift.This article will give you a comprehensive guide to Amazon Aurora and Amazon Redshift. You will explore how you can utilize AWS Glue to move data from Aurora to Redshift using 7 easy steps. You will also get to know about the advantages and limitations of this method in further sections. Let’s get started. Prerequisites You will have a much easier time understanding the method of connecting Aurora to Redshift if you have gone through the following aspects: An active account in AWS. Working knowledge of Database and Data Warehouse. Basic knowledge of ETL process. Introduction to Amazon Aurora Aurora is a database engine that aims to provide the same level of performance and speed as high-end commercial databases, but with more convenience and reliability. One of the key benefits of using Amazon Aurora is that it saves DBAs (Database Administrators) time when designing backup storage drives because it backs up data to AWS S3 in real-time without affecting the performance. Moreover, it is MySQL 5.6 compliant and provides five times the throughput of MySQL on similar hardware. To know more about Amazon Aurora, visit this link. Introduction to Amazon Redshift Amazon Redshift is a cloud-based Data Warehouse solution that makes it easy to combine and store enormous amounts of data for analysis and manipulation. Large-scale database migrations are also performed using it. The Redshift architecture is made up of several computing resources known as Nodes, which are then arranged into Clusters. The key benefit of Redshift is its great scalability and quick query processing, which has made it one of the most popular Data Warehouses even today. To know more about Amazon Redshift, visit this link. Introduction to AWS Glue AWS Glue is a serverless ETL service provided by Amazon. Using AWS Glue, you pay only for the time you run your query. In AWS Glue, you create a metadata repository (data catalog) for all RDS engines including Aurora, Redshift, and S3, and create connection, tables, and bucket details (for S3). You can build your catalog automatically using a crawler or manually. Your ETL internally generates Python/Scala code, which you can customize as well. Since AWS Glue is serverless, you do not have to manage any resources and instances. AWS takes care of it automatically. To know more about AWS Glue, visit this link. Simplify ETL using LIKE.TG ’s No-code Data Pipeline LIKE.TG Data helps you directly transfer data from 100+ data sources (including 30+ free sources) to Business Intelligence tools, Data Warehouses, or a destination of your choice in a completely hassle-free automated manner. LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. LIKE.TG takes care of all your data preprocessing needs required to set up the integration and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. Get Started with LIKE.TG for Free Check out what makes LIKE.TG amazing: Real-Time Data Transfer: LIKE.TG with its strong Integration with 100+ Sources (including 30+ Free Sources), allows you to transfer data quickly efficiently. This ensures efficient utilization of bandwidth on both ends.Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Tremendous Connector Availability: LIKE.TG houses a large variety of connectors and lets you bring in data from numerous Marketing SaaS applications, databases, etc. such as HubSpot, Marketo, MongoDB, Oracle, Salesforce, Redshift, etc. in an integrated and analysis-ready form.Simplicity: Using LIKE.TG is easy and intuitive, ensuring that your data is exported in just a few clicks.Completely Managed Platform: LIKE.TG is fully managed. You need not invest time and effort to maintain or monitor the infrastructure involved in executing codes.Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls. Sign up here for a 14-Day Free Trial! Steps to Move Data from Aurora to Redshift using AWS Glue You can follow the below-mentioned steps to connect Aurora to Redshift using AWS Glue: Step 1: Select the data from Aurora as shown below. Step 2: Go to AWS Glue and add connection details for Aurora as shown below. Similarly, add connection details for Redshift in AWS Glue using a similar approach. Step 3: Once connection details are created create a data catalog for Aurora and Redshift as shown by the image below. Once the crawler is configured, it will look as shown below: Step 4: Similarly, create a data catalog for Redshift, you can choose schema name in the Include path so that the crawler only creates metadata for that schema alone. Check the content of the Include path in the image shown below. Step 5: Once both the data catalog and data connections are ready, start creating a job to export data from Aurora to Redshift as shown below. Step 6: Once the mapping is completed, it generates the following code along with the diagram as shown by the image below. Once the execution is completed, you can view the output log as shown below. Step 7: Now, check the data in Redshift as shown below. Advantages of Moving Data using AWS Glue AWS Glue has significantly eased the complicated process of moving data from Aurora to Redshift. Some of the advantages of using AWS Glue for moving data from Aurora to Redshift include: The biggest advantage of using this approach is that it is completely serverless and no resource management is needed. You pay only for the time of query and based on the data per unit (DPU) rate. If you moving high volume data, you can leverage Redshift Spectrum and perform Analytical queries using external tables. (Replicate data from Aurora and S3 and hit queries over) Since AWS Glue is a service provided by AWS itself, this can be easily coupled with other AWS services i.e., Lambda and Cloudwatch, etc to trigger the next job processing or for error handling. Limitations of Moving Data using AWS Glue Though AWS Glue is an effective approach to move data from Aurora to Redshift, there are some limitations associated with it. Some of the limitations of using AWS Glue for moving Data from Aurora to Redshift include: AWS Glue is still a new AWS service and is in the evolving stage. For complex ETL logic, it may not be recommended. Choose this approach based on your Business logic AWS Glue is still available in the limited region. For more details, kindly refer to AWS documentation. AWS Glue internally uses Spark environment to process the data hence you will not have any other option to select any other environment if your business/use case demand so. Invoking dependent job and success/error handling requires knowledge of other AWS data services i.e. Lambda, Cloudwatch, etc. Conclusion The approach to use AWS Glue to set up Aurora to Redshift integration is quite handy as this avoids doing instance setup and other maintenance. Since AWS Glue provides data cataloging, if you want to move high volume data, you can move data to S3 and leverage features of Redshift Spectrum from the Redshift client. However, unlike usingAWS DMSto move Aurora to Redshift, AWS Glue is still in an early stage. Job and multi-job handling or error handling requires a good knowledge of other AWS services. On the other hand in DMS, you just need to set up replication instances and tasks, and not much handling is needed. Another limitation with this method is that AWS Glue is still in a few selected regions. So, all these aspects need to be considered in choosing this procedure for migrating data from Aurora to Redshift. If you are planning to use AWS DMS to move data from Aurora to Redshift then you can check out our article to explore the steps to move Aurora to Redshift using AWS DMS. Visit our Website to Explore LIKE.TG Businesses can use automated platforms like LIKE.TG Data to set this integration and handle the ETL process. It helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you a hassle-free experience. Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs. Share your experience of connecting Aurora to Redshift using AWS Glue in the comments section below!
 SFTP/FTP to BigQuery: 2 Easy Methods
SFTP/FTP to BigQuery: 2 Easy Methods
Many businesses generate data and store it in the form of a file. However, the data stored in these files can not be used as it is for analysis. Given data is now the new oil, businesses need a way to move data into a database or data warehouse so that they can leverage the power of a SQL-like language to answer their key questions in a matter of seconds. This article talks about loading the data stored in files on FTP to BigQuery Data Warehouse.Introduction to FTP FTP stands for File Transfer Protocol, which is the standard protocol used to transfer files from one machine to another machine over the internet. When downloading an mp3 from the browser or watching movies online, have you encountered a situation where you are provided with an option to download the file from a specific server? This is FTP in action. FTP is based on a client-server architecture and uses two communication channels to operate: A command channel that contains the details of the requestA data channel that transmits the actual file between the devices Using FTP, a client can upload, download, delete, rename, move and copy files on a server. For example, businesses like Adobe offer their software downloads via FTP. Introduction to Google BigQuery Bigquery is a NoOps (No operations) data warehouse as a service provided by Google to their customers to process over petabytes of data in seconds using SQL as a programming language. BigQuery is a cost-effective, fully managed, serverless, and highly available service. Since Bigquery is fully managed, it takes the burden of implementation and management off the user, making it super easy for them to focus on deriving insights from their data. You can read more about the features of BigQuery here. Moving Data from FTP Server To Google BigQuery There are two ways of moving data from FTP Server to BigQuery: Method 1: Using Custom ETL Scripts to Move Data from FTP to BigQuery To be able to achieve this, you would need to understand how the interfaces of both FTP and BigQuery work, hand-code custom scripts to extract, transform and load data from FTP to BigQuery. This would need you to deploy tech resources. Method 2: Using LIKE.TG Data to Move Data from FTP to BigQuery The same can be achieved using a no-code data integration product like LIKE.TG Data. LIKE.TG is fully managed and can load data in real-time from FTP to BigQuery. This will allow you to stop worrying about data and focus only on deriving insights from it. Get Started with LIKE.TG for Free This blog covers both approaches in detail. It also highlights the pros and cons of both approaches so that you can decide on the one that suits your use case best. Methods to Move Data from FTP to BigQuery These are the methods you can use to move data from FTP to BigQuery in a seamless fashion: Method 1: Using Custom ETL Scripts to Move Data from FTP to BigQueryMethod 2: Using LIKE.TG Data to Move Data from FTP to BigQuery Download the Cheatsheet on How to Set Up High-performance ETL to BigQuery Learn the best practices and considerations for setting up high-performance ETL to BigQuery Method 1: Using Custom ETL Scripts to Move Data from FTP to BigQuery The steps involved in loading data from FTP Server to BigQuery using Custom ETL Scripts are as follows: Step 1: Connect to BigQuery Compute EngineStep 2: Copy Files from Your FTP ServerStep 3: Load Data into BigQuery using BQ Load Utility Step 1: Connect to BigQuery Compute Engine Download the WINSCP tool for your device.Open WinSCP application to connect to the Compute Engine instance.In the session, the section select ‘FTP’ as a file protocol.Paste external IP in Host Name.Use key-comment as a user name. Lastly, click on the login option. Step 2: Copy Files from Your FTP Server On successful login, copy the file to VM. Step 3: Load Data into BigQuery using BQ Load Utility (In this article we are loading a “.CSV” file) 1. SSH into your compute engine VM instance, go to the directory in which you have copied the file. 2. Execute the below command bq load --autodetect --source_format=CSV test.mytable testfile.csv For more bq options please read the bq load CLI command google documentation. 3. Now verify the data load by selecting data from the “test.mytable” table by opening the BigQuery UI. Thus we have successfully loaded data in the BigQuery table using FTP. Limitations of Using Custom ETL Scripts to Move Data from FTP to BigQuery Here are the limitations of using Custom ETL Scripts to move data from FTP to BigQuery: The entire process would have to be set up manually. Additionally, once the infrastructure is up, you would need to provide engineering resources to monitor FTP server failure, load failure, and more so that accurate data is available in BigQuery.This method works only for a one-time load. If your use case is to do a change data capture, this approach will fail.For loading data in UPSERT mode will need to write extra lines of code to achieve this functionality.If the file contains any special character or unexpected character data load will fail.Currently, bq load supports only a single character delimiter, if we have a requirement of loading multiple characters delimited files, this process will not work.Since in this process, we are using multiple applications, so in case of any process, abortion backtracking will become difficult. Method 2: Using LIKE.TG Data to Move Data from FTP to BigQuery A much more efficient and elegant way would be to use a ready platform like LIKE.TG (14-day free trial) to load data from FTP (and a bunch of other data sources) into BigQuery.LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. Sign up here for a 14-Day Free Trial! LIKE.TG takes care of all your data preprocessing to set up migration from FTP Data to BigQuery and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. LIKE.TG can help you bring data from FTP to BigQuery in two simple steps: Configure Source: Connect LIKE.TG Data with SFTP/FTP by providing a unique name for your Pipeline, Type, Host, Port, Username, File Format, Path Prefix, Password. Configure Destination:Connect to your BigQuery account and start moving your data from FTP to BigQuery by providingthe project ID, dataset ID, Data Warehouse name, GCS bucket. Step 2: Authenticate and point to the BigQuery Table where the data needs to be loaded.That is all. LIKE.TG will ensure that your FTP data is loaded to BigQuery in real-time without any hassles. Here are some of the advantages of using LIKE.TG : Easy Setup and Implementation – Your data integration project can take off in just a few mins with LIKE.TG .Complete Monitoring and Management – In case the FTP server or BigQuery data warehouse is not reachable, LIKE.TG will re-attempt data loads in a set instance ensuring that you always have accurate data in your data warehouse.Transformations– LIKE.TG provides preload transformations through Python code. It also allows you to run transformation code for each event in the pipelines you set up. You need to edit the event object’s properties received in the transform method as a parameter to carry out the transformation. LIKE.TG also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use.Connectors– LIKE.TG supports 100+ integrations to SaaS platforms, files, databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; and MySQL, MongoDB, TokuDB, DynamoDB, PostgreSQL databases to name a few.Change Data Capture – LIKE.TG can automatically detect new files on the FTP location and load them to BigQuery without any manual intervention100’s of additional Data Sources – In addition to FTP, LIKE.TG can bring data from 100’s other data sources into BigQuery in real-time. This will ensure that LIKE.TG is the perfect companion for your businesses’ growing data integration needs24×7 Support – LIKE.TG has a dedicated support team available at all points to swiftly resolve any queries and unblock your data integration project. Conclusion This blog talks about the two methods you can implement to move data from FTP to BigQuery in a seamless fashion. Extracting complex data from a diverse set of data sources can be a challenging task and this is where LIKE.TG saves the day! Visit our Website to Explore LIKE.TG LIKE.TG offers a faster way to move data from Databases or SaaS applications like FTP into your Data Warehouse like Google BigQuery to be visualized in a BI tool. LIKE.TG is fully automated and hence does not require you to code. Sign Up for a 14-day free trial to try LIKE.TG for free. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
 HubSpot to BigQuery: Move Data Instantly
HubSpot to BigQuery: Move Data Instantly
Need a better way to handle all that customer and marketing data in HubSpot. Transfer it to BigQuery. Simple! Want to know how?This article will explain how you can transfer your HubSpot data into Google BigQuery through various means, be it HubSpot’s API or an automated ETL tool like LIKE.TG Data, which does it effectively and efficiently, ensuring the process runs smoothly. What is HubSpot? HubSpot is an excellent cloud-based platform for blending different business functions like sales, marketing, support, etc. It features five different hubs: service, Operations, CRM, Marketing, and CMS. The marketing hub is used for campaign automation and lead generation, while the sales hub assists in automating sales pipelines, giving an overview of all contacts at a glance. It’s also an excellent way to include a knowledge base, generate feedback from the consumer, and construct interactive support pages. What is BigQuery? Google BigQuery is a fully managed and serverless enterprise cloud data warehouse. It uses Dremel technology, which transforms SQL queries into tree structures. BigQuery provides an outstanding query performance owing to its column-based storage system. BigQuery offers multiple features—one is the built-in BigQuery Data Transfer Service, which moves data automatically, while another is BigQuery ML, which runs machine learning models. BigQuery GIS enables geospatial analysis, while the fast query processing is enabled by BigQuery BI Engine, rendering it a powerful tool for any data analysis task. Migrate your Data from HubSpot to BigQueryGet a DemoTry itMigrate your Data from Google Ads to BigQueryGet a DemoTry itMigrate your Data from Google Analytics 4 to BigQueryGet a DemoTry it Need to Move Data from HubSpot to BigQuery Moving HubSpot data to BigQuery creates a single source of truth that aggregates information to deliver accurate analysis. Therefore, you can promptly understand customers’ behavior and improve your decision-making concerning business operations. BigQuery can manage huge amounts of data with ease. If there is a need for your business expansion and the production of data increases, BigQuery will be there, making it easy for you. BigQuery, built on Google Cloud, has robust security features like auditing, access controls, and data encryption. User data is kept securely and compliant with the rules, thus making it safe for you. BigQuery’s flexible pricing model can lead to major cost savings compared to having an on-premise data warehouse you pay to maintain. Here’s a list of the data that you can move from HubSpot to BigQuery: Activity data (clicks, views, opens, URL redirects, etc.) Calls-to-action (CTA) analytics Contact lists CRM data Customer feedback Form submission data Marketing emails Sales data Prerequisites When moving your HubSpot data to BigQuery manually, make sure you have the following set up: An account with billing enabled on Google Cloud Platform. Admin access to a HubSpot account. You have the Google Cloud CLI installed. Connect your Google Cloud project to the Google Cloud SDK. Activate the Google Cloud BigQuery API. Make sure you have BigQuery Admin permissions before loading data into BigQuery. These steps ensure you’re all set for a smooth data migration process! Methods to move data from HubSpot to BigQuery Method1: How to move data from HubSpot to BigQuery Using HubSpot Private App Step 1: Creating a Private App 1. a) Go to the Settings of your HubSpot account and select Integrations → Private Apps. Click on Create a Private App. 1. b) On the Basic Info page, provide basic app details: Enter your app name or click on Generate a new random name You can also upload a logo by hovering over the logo placeholder, or by default, the initials of your private app name will be your logo. Enter the description of your app, or leave it empty as you wish. However, it is best practice to provide an apt description. 1. c) Click on the Scopes tab beside the Basic Info button. You can configure Read, Write, or give permissions for both. Suppose I want to transfer only the contact information stored on my HubSpot data into BigQuery. I will select only Read configurations, as shown in the attached screenshot. Note: If you access some sensitive data, it will also showcase a warning message, as shown below. 1. d) Once configuring your permissions, click the Create App button at the top right. 1. e) After selecting the Continue Creating button, a prompt screen with your Access token will appear. Once you click on Show Token, you can Copy your token. Note: Keep your access token handy; we will require that for the next step. Your Client Secret is not needed. Step 2: Making API Calls with your Access Token Open up your command line and type in: curl --request GET --url https://api.hubapi.com/contacts/v1/lists/all/contacts/all --header "Authorization: Bearer (Your_Token)" --header "Content-Type: application/json" Just replace (Your_Token) with your actual access token id. Here’s what the response will look like: { "contacts": [ { "vid": 33068263516, "canonical-vid":33068263516, "merged-vids":[], "portal-id":46584864, "is-contact":true, "properties": { "firstname":{"value":"Sam from Ahrefs"}, "lastmodifieddate":{"value":"1719312534041"} }, }, NOTE: If you prefer not to use the curl command, use JavaScript. To get all the contacts created in your HubSpot account with Node.js and Axios, your request will look like this: axios.get('https://api.hubapi.com/crm/v3/objects/contacts', { headers: { 'Authorization': `Bearer ${YOUR_TOKEN}`, 'Content-Type': 'application/json' } }) .then((response) => { // Handle the API response }) .catch((error) => { console.error(error); }); Remember, the private app access tokens are implemented on OAuth. You can also authenticate calls using any HubSpot client library. For instance, with the Node.js client library, you pass your app’s access token like this: const hubspotClient = new hubspot.Client({ accessToken: YOUR_TOKEN }); Step 3: Create a BigQuery Dataset From your Google Cloud command line, run this command: bq mk hubspot_dataset hubspot_dataset is just a name that I have chosen. You can change it accordingly. The changes will automatically be reflected in your Google Cloud console. Also, a message “Dataset ‘united-axle-389521:hubspot_dataset’ successfully created.” will be displayed in your CLI. NOTE: Instead of using the Google command line, you can also create a dataset from the console. Just hover over View Actions on your project ID. Once you click it, you will see a Create Dataset option. Step4: Create an Empty Table Run the following command in your Google CLI: bq mk --table --expiration 86400 --description "Contacts table" --label organization:development hubspot_dataset.contacts_table After your table is successfully created, a message “Table ‘united-axle-389521:hubspot_dataset.contacts_table’ successfully created” will be displayed. The changes will also be reflected in the cloud console. NOTE: Alternatively, you can create a table from your BigQuery Console. Once your dataset has been created, click on View Actions and select Create Table. After selecting Create Table, a new table overview page will appear on the right of your screen. You can create an Empty Table or Upload a table from your local machine, such as Drive, Google Cloud Storage, Google Bigtable, Amazon S3, or Azure Blob Storage. Step 5: Adding Data to your Empty Table Before you load any data into BigQuery, you’ll need to ensure it’s in a format that BigQuery supports. For example, if the API you’re pulling data from returns XML, you’ll need to transform it into a format BigQuery understands. Currently, these are the data formats supported by BigQuery: Avro JSON (newline delimited) CSV ORC Parquet Datastore exports Firestore exports You also need to ensure that your data types are compatible with BigQuery. The supported data types include: ARRAY BOOLEAN BYTES DATE DATETIME GEOGRAPHY INTERVAL JSON NUMERIC RANGE STRING STRUCT TIME TIMESTAMP See the documentation’s“DataTypes” and “Introduction to loading data” pages for more details. The bq load command is your go-to for uploading data to your BigQuery dataset, defining schema, and providing data type information. You should run this command multiple times to load all your tables into BigQuery. Here’s how you can load a newline-delimited JSON file contacts_data.json from your local machine into the hubspot_dataset.contacts_table: bq load \ --source_format=NEWLINE_DELIMITED_JSON \ hubspot_dataset.contacts_table \ ./contacts_data.json \ ./contacts_schema.json Since you’re loading files from your local machine, you must specify the data format explicitly. You can define the schema for your contacts in the local schema file contacts_schema.json. Step 6: Scheduling Recurring Load Jobs 6. a) First, create a directory for your scripts and an empty backup script: $ sudo mkdir /bin/scripts/ touch /bin/scripts/backup.sh 6. b) Next, add the following content to the backup.sh file and save it: #!/bin/bash bq load --autodetect --replace --source_format=NEWLINE_DELIMITED_JSON hubspot_dataset.contacts_table ./contacts_data.json 6. c) Let’s edit the crontab to schedule this script. From your CLI, run: $ crontab -e 6.d) You’ll be prompted to edit a file where you can schedule tasks. Add this line to schedule the job to run at 6 PM daily: 0 18 * * * /bin/scripts/backup.sh 6. e) Finally, navigate to the directory where your backup.sh file is located and make it executable: $ chmod +x /bin/scripts/backup.sh And there you go! These steps ensure that cron runs your backup.sh script daily at 6 PM, keeping your data in BigQuery up-to-date. Limitations of the Manual Method to Move Data from HubSpot to BigQuery HubSpot APIs have a rate limit of 250,000 daily calls that resets every midnight. You can’t use wildcards, so you must load each file individually. CronJobs won’t alert you if something goes wrong. You need to set up separate schemas for each API endpoint in BigQuery. Not ideal for real-time data needs. Extra code is needed for data cleaning and transformation. Method 2: Using LIKE.TG Data to Move Data from HubSpot to BigQuery These challenges can be pretty frustrating; I’ve been there. The manual method comes with its own set of hurdles and limitations. To avoid all these, you can easily opt for SaaS alternatives such as LIKE.TG Data. In three easy steps, you can configure LIKE.TG Data to transfer your data from HubSpot to BigQuery. Step1: Setup HubSpot as a Source Connector To connect your HubSpot account as a source in LIKE.TG , search for HubSpot. Configure your HubSpot Source. Give your pipeline a name, configure your HubSpot API Version, and mention how much Historical Sync Duration you want, such as for the past three months, six months, etc. You can also choose to load all of your Historical data. For example, I will select three months and then click on Continue. Next, your objects will be fetched, and you can select them per your requirements. By default, all of your objects are selected. However, you can choose your objects accordingly. For example, I will select only my contacts. You can also search for your objects by clicking the panel’s Search icon at the top-right-hand side and then clicking Continue. Step2: Setup BigQuery as Destination Connector Select BigQuery as your destination. Configure your destination by giving a Destination Name, selecting your type of account, i.e., User Account or Service Account, and mentioning your Project ID. Then click on Save Continue. NOTE: As the last step, you can add a Destination Table Prefix, which will be reflected on your destination. For example, if you put ‘hs,’ all the tables loaded into your BigQuery from HubSpot will have ‘hs_original-table-name.’ If you have JSON files, manually flattening your files is a tedious process; thus, LIKE.TG Data provides you with two options: JSON fields as JSON strings and array fields to strings, while the other is collapsing nested arrays into strings. You can select either one of those and click on Continue. Once you’re done, your HubSpot data will be loaded into Google BigQuery. Step 3: Sync your HubSpot Data to BigQuery In the pipeline lifecycle, you can observe your source being connected, data being ingested, prepared for loading into BigQuery, and finally, the actual loading of your HubSpot data. As you can see above, our HubSpot has now been connected to BigQuery. Once all events have loaded, your final page will resemble this. It is much easier to adjust your loads or ingestion schedule using our interface. You can also include any object for historical load after creating your pipeline. You can also include objects for ingestion only. Moreover, on the same platform, you can perform additional alterations to your data, such as changing schemas and carrying out ad-hoc analyses immediately after data loads. Our excellent support team is on standby for any queries you may have. What are some of the reasons for using LIKE.TG Data? Exceptional Security: It’s fault-tolerant architecture guarantees that no information or data will be lost, so you need not worry. Scalability: LIKE.TG Data for scale is developed to be scaled out at a fraction of the cost with almost zero delay, making it suitable for contemporary extensive data requirements. Built-in Connectors: LIKE.TG Data has more than 150 connectors, including HubSpot as a source and Google BigQuery as a destination, databases, and SaaS platforms; it even has a built-in webhook and RESTful API connector designed specifically for custom sources. Incremental Data Load: It utilizes bandwidth efficiently by only transferring modified data in real time. Auto Schema Mapping: LIKE.TG Data manages schema automatically by detecting incoming data format and copying it to the destination schema. You can select between full and incremental mappings according to your data replication needs. Easy to use: LIKE.TG Data offers a no-code ETL or ELT load pipeline platform. Conclusion HubSpot is a key part of many businesses’ tech stack, enhancing customer relationships and communication strategies—your business growth potential skyrockets when you combine HubSpot data with other sources. Moving your data lets you enjoy a single source of truth, which can significantly boost your business growth. We’ve discussed two methods to move data—the manual process, which requires a lot of configuration and effort. Instead, try LIKE.TG Data—it does all the heavy lifting for you with a simple, intuitive process. LIKE.TG Data helps you integrate data from multiple sources like HubSpot and load it into BigQuery for real-time analysis. It’s user-friendly, reliable, and secure and makes data transfer hassle-free. Sign up for a 14-day free trial with LIKE.TG and connect Hubspot to BigQuery in minutes. Also, check out LIKE.TG ’s unbeatable pricing or get a custom quote for your requirements. FAQs Q1. How often can I sync my HubSpot data with BigQuery? You can sync your HubSpot data with BigQuery as often as needed. With tools such as LIKE.TG Data, you can set up real-time to keep your data up-to-date. Q2. What are the costs associated with this integration? The costs for integrating HubSpot with BigQuery depend on the tool you use and the amount of data you’re transferring. LIKE.TG Data offers a flexible pricing model. Our prices can help you better understand. BigQuery costs are based on the amount of data stored and processed. Q3. How secure is the data transfer process? The data transfer process is highly secure. LIKE.TG Data ensures data security with its fault-tolerant architecture, access controls, data encryption, and compliance with industry standards, ensuring your data is always protected throughout the transfer. Q4. What support options are available if I encounter issues? LIKE.TG Data offers extensive support options, including detailed documentation, a dedicated support team through our Chat support available 24×5, and community forums. If you run into any issues, you can easily reach out for assistance to ensure a smooth data integration process.
 Load Data from Freshdesk to Redshift in 2 East Steps
Load Data from Freshdesk to Redshift in 2 East Steps
Are you looking to load data from Freshdesk to Redshift for deeper analysis? Or are you looking to simply create a backup of this data in your warehouse? Whatever be the use case, deciding to move data from Freshdesk to Redshift is a step in the right direction. This blog highlights the broad approaches and steps that one would need to take to reliably load data from Freshdesk to Redshift.What is Freshdesk? Freshdesk is a cloud-based customer support platform owned by Freshworks. It integrates support platforms such as emails, live chat, phone and social media platforms like Twitter and Facebook. Freshworks allows you to keep track of all ongoing tickets and manage all support-related communications across all platforms. Freshdesk generates reports that allow you to understand your team’s performance and gauge the customers’ satisfaction level. Freshdesk offers well-defined and rich REST (Representation State Transfer) API. Using Freshdesk’s REST API, data on Freshdesk tickets, customer support, team’s performance, etc. can be extracted and loaded onto Redshift for deeper analysis. Solve your data replication problems with LIKE.TG ’s reliable, no-code, automated pipelines with 150+ connectors.Get your free trial right away! What is Amazon Redshift? Amazon Redshift is a data warehouse owned and maintained by amazon web services (AWS) and forms a large part of the AWS cloud computing platform. It is built using MPP (massively parallel processing) architecture. Its ability to handle analytical workloads on a large volume of data sets stored in the column-oriented DBMS principles makes it different from Amazon’s other hosted database offerings. Redshift makes it possible to query megabytes of structured and non-structured data using SQL. You can save the results back to your S3 data lake using formats like Apache Parquet. This allows you to further analyze from other analytical services like Amazon Athena, Amazon EMR, and Amazon SageMaker. Find out more on Amazon Redshift Data Warehouse here. Methodsto Load Data from Freshdesk to Redshift This can be done in two ways: Method 1: Loading Data from Freshdesk to Redshift Using Custom ETL Scripts This would need you to invest in the engineering team’s bandwidth to build a custom solution. The process involves the following steps broadly. Getting data out using Freshdesk API, preparing Freshdesk data, and finally loading data into Redshift. Method 2: Load Data from Freshdesk to Redshift Using LIKE.TG LIKE.TG comes with out-of-the-box integration with Freshdesk (Free Data Source) and loads data to Redshift without having to write any code. LIKE.TG ’s ability to reliably load data in real-time combined with its ease of use makes it a great alternative to Method 1. Get Started with LIKE.TG for Free Methodsto Load Data from Freshdesk to Redshift Method 1: Loading Data from Freshdesk to Redshift Using Custom ETL ScriptsMethod 2: Load Data from Freshdesk to Redshift Using LIKE.TG This article will provide an overview of both the above approaches. This will allow you to analyze the pros and cons of all approaches and select the best method as per your use case. Method 1: Loading Data from Freshdesk to Redshift Using Custom ETL Scripts Step 1: Getting Data from Freshdesk The REST API provided by Freshdesk allows you to get data on agents, tickets, companies and any other information from their back-end. Most of the API calls are simple, for example, you could call GET /api/v2/tickets to list all tickets. Optional filters such as company ID, and updated date could be used to limit retrieved data. The include parameter could also be used to fetch fields that are not sent by default. Freshdesk Sample Data The information is returned in JSON format. Each JSON object may contain more than one attribute which should be parsed before loading the data in your data warehouse. Below is an example of the API call response made to return all tickets. { "cc_emails" : ["[email protected]"], "fwd_emails" : [ ], "reply_cc_emails" : ["[email protected]"], "email_config_id" : null, "fr_escalated" : false, "group_id" : null, "priority" : 1, "requester_id" : 1, "responder_id" : null, "source" : 2, "spam" : false, "status" : 2, "subject" : "", "company_id" : 1, "id" : 20, "type" : null, "to_emails" : null, "product_id" : null, "created_at" : "2015-08-24T11:56:51Z", "updated_at" : "2015-08-24T11:59:05Z", "due_by" : "2015-08-27T11:30:00Z", "fr_due_by" : "2015-08-25T11:30:00Z", "is_escalated" : false, "description_text" : "Not given.", "description" : "<div>Not given.</div>", "custom_fields" : { "category" : "Primary" }, "tags" : [ ], "requester": { "email": "[email protected]", "id": 1, "mobile": null, "name": "Rachel", "phone": null }, "attachments" : [ ] } Step 2: Freshdesk Data Preparation You should create a data schema to store the retrieved data. Freshdesk documentation provides the data types to use, for example, INTEGER, FLOAT, DATETIME, etc. Some of the retrieved data may not be “flat” – they maybe list. Therefore, to capture unpredictable cardinality in each of the records, additional tables may need to be created. Step 3: Loading Data to Redshift When you have high volumes of data to be stored, you should load data into Amazon S3 and load into Redshift using the copy command. Often times when dealing with low volumes of data, you may think of loading the data using the INSERT statement. This will load the data row by row and slow the process because Redshift isn’t optimized to load data in this way. Freshdesk to Redshift Using Custom Code: Limitations and Challenges Accessing Freshdesk Data in Real-time: At this stage, you have successfully created a program that loads data into the data warehouse. The challenge of loading new or updated data is not solved yet. You could decide to replicate data in real-time, each time a new or updated record is created. This process will be slow and resource-intensive. You will need to write additional code and build cron jobs to run this in a continuous loop to get new and updated data as it appears in the Freshdesk.Infrastructure Maintainance: Always remember that any code that is written should be maintained because Freshdesk may modify its API or a datatype that your script doesn’t recognize may be sent by the API. Method 2: Load Data from Freshdesk to Redshift Using LIKE.TG A more elegant, hassle-free alternative to loading data from Freshdesk (Free Data Source) to Redshift would be to use a Data Integration Platform like LIKE.TG (14-day free trial) that works out of the box. Being a no-code platform, LIKE.TG can overcome all the limitations mentioned above and seamlessly and securely more Freshdesk data to Redshift in just two steps: Authenticate and Connect Freshdesk Data SourceConfigure the Redshift Data warehouse where you need to move the data Sign up here for a 14-Day Free Trial! Advantages of Using LIKE.TG The LIKE.TG data integration platform lets you move data from Freshdesk (Free Data Source) to Redshift seamlessly. Here are some other advantages: No Data Loss – LIKE.TG ’s fault-tolerant architecture ensures that data is reliably moved from Freshdesk to Redshift without data loss.100’s of Out of the Box Integrations – In addition to Freshdesk, LIKE.TG can bring data from 100+ Data Sources (Including 30+ Free Data Sources)into Redshift in just a few clicks. This will ensure that you always have a reliable partner to cater to your growing data needs.Minimal Setup – Since LIKE.TG is a fully managed, setting up the platform would need minimal effort and bandwidth from your end.Automatic schema detection and mapping – LIKE.TG automatically scans the schema of incoming Freshdesk data. If any changes are detected, it handles this seamlessly by incorporating this change on Redshift.Exceptional Support – LIKE.TG provides 24×7 support to ensure that you always have Technical support for LIKE.TG is provided on a 24/7 basis over both email and Slack. As an alternate option, if you use Google BigQuery, you can also load your data from Freshdesk to Google BigQuery using this guide here. Conclusion This article teaches you how to set up Freshdesk to Redshift Data Migration with two methods. It provides in-depth knowledge about the concepts behind every step to help you understand and implement them efficiently. The first method, however, can be challenging especially for a beginner this is where LIKE.TG saves the day.LIKE.TG Data, a No-code Data Pipeline, helps you transfer data from a source of your choice in a fully-automated and secure manner without having to write the code repeatedly. Visit our Website to Explore LIKE.TG LIKE.TG , with its strong integration with100+ sources BI tools, allows you to not only export load data but also transform enrich your data make it analysis-ready in a jiff. Want to take LIKE.TG for a spin?Sign Up here for the 14-day free trialand experience the feature-rich LIKE.TG suite first hand. Tell us about your experience of setting up Freshdesk to Redshift Data Transfer! Share your thoughts in the comments section below!
 Google Analytics to PostgreSQL: 2 Easy Methods
Google Analytics to PostgreSQL: 2 Easy Methods
Even though Google provides a comprehensive set of analysis tools to work with data, most organizations will need to pull the raw data into their on-premise database. This is because having it in their control allows them to combine it with their customer and product data to perform a much deeper analysis. This post is about importing data from Google Analytics to PostgreSQL – one of the very popular relational databases in the market today. This blog covers two approaches for integrating GA with PostgreSQL – The first approach talks about using an automation tool extensively. Alternatively, the blog also covers the manual method for achieving the integration. Methods to Connect Google Analytics to PostgreSQL Method 1: Using LIKE.TG Data to Connect Google Analytics to PostgreSQL LIKE.TG is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates flexible data pipelines to your needs. With integration with 150+ Data Sources (40+ free sources), including Google Analytics, we help you not only export data from sources load data to the destinations but also transform enrich your data, make it analysis-ready. Get Started with LIKE.TG for Free Method 2: Using Manual ETL Scripts to Connect Google Analytics to PostgreSQL Manually coding custom ETL (extract, transform, load) scripts enables precise customization of the data transfer process, but requires more development effort compared to using automated tools. Method 1: Using LIKE.TG Data to Connect Google Analytics to PostgreSQL The best way to connect Google Analytics to PostgreSQL is to use a Data Pipeline Platform like LIKE.TG (14-day free trial) that works out of the box. LIKE.TG can help you import data from Google Analytics to PostgreSQL for free in two simple steps: Step 1: Connect LIKE.TG to Google Analytics to set it up as your source by filling in the Pipeline Name, Account Name, Property Name, View Name, Metrics, Dimensions, and the Historical Import Duration. Step 2: Load data from Google Analytics to Postgresql by providing your Postgresql databases credentials like Database Host, Port, Username, Password, Schema, and Name along with the destination name. LIKE.TG will do all the heavy lifting to ensure that your data is securely moved from Google Analytics to PostgreSQL. LIKE.TG automatically handles all the schema changes that may happen at Google Analytics’ end. This ensures that you have a dependable infrastructure that delivers error-free data in PostgreSQL at all points. Here are a few benefits of using LIKE.TG : Easy-to-use Platform: LIKE.TG has a straightforward and intuitive UI to configure the jobs. Transformations: LIKE.TG provides preload transformations through Python code. It also allows you to run transformation code for each event in the Data Pipelines you set up. You need to edit the event object’s properties received in the transform method as a parameter to carry out the transformation. LIKE.TG also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use. Real-time Data Transfer: Support for real-time synchronization across a variety of sources and destinations. Automatic Schema Mapping: LIKE.TG can automatically detect your source’s schema type and match it with the schema type of your destination. Solve your data integration problems with LIKE.TG ’s reliable, no-code, automated pipelines with 150+ connectors.Get your free trial right away! Method 2: Using Manual ETL Scripts to Connect Google Analytics to PostgreSQL In this method of moving data from Google Analytics to PostgreSQL, you will first need to get data from Google Analytics followed by accessing Google Reporting API V4 as mentioned in the following section. Getting data from Google Analytics Click event data from Google Analytics can be accessed through Reporting API V4. There are two sets of Rest APIs in Reporting API V4 tailor-made for specific use cases. Metrics API – These APIs allow users to get aggregated analytics information on user behavior based on available dimensions. Dimensions are the attributes based on which metrics are aggregated. For example, if time is a dimension and the number of users in a specific time will be a metric. User Activity API – This API allows you to access information about the activities of a specific user. Knowledge of the user ID is required in this case. To get the user IDs of people accessing your page, you will need to modify some bits in the client-side Google Analytics function that you are going to use and capture the client ID. This information is not exactly available in the Google developer documentation, but there is ample online documentation about it. Ensure you consult the laws and restrictions in your local country before attempting this since its legality will depend on the country’s privacy laws. After changing the client script, you must also register the user ID as a custom dimension in the Google Analytics dashboard. Google Analytics APIs use oAuth 2.0 as the authentication protocol. Before accessing the APIs, the user first needs to create a service account in the Google Analytics dashboard and generate authentication tokens. Let us review how this can be done. Go to the Google service accounts page and select a project. If you have not already created a project, please create one. Click on Create Service Account. You can ignore the permissions for this exercise. On the ‘Grant users access to this service account’ section, click Create key. Select JSON as the format for your key. Click create a key and you will be prompted with a dialogue to save the key on your local computer. Save the key. We will be using the information from this step when we actually access the API. Accessing Google Reporting API V4 Google provides easy-to-use libraries in Python, Java, and PHP to access its reporting APIs. These libraries are the preferred method to download the data since the authentication procedure and the complex JSON response format makes it difficult to access these APIs using command-line tools like CURL. Detailed documentation of this API can be found here. Here the python library is used to access the API. The following steps and code snippets explain the procedure to load data from Google Analytics to PostgreSQL: Step 1: Installing the Python GA Library to Your Environment Step 2: Importing the Required Libraries Step 3: Initializing the Required Variables for OAuth Authentication Step 4: Building the Required Objects Step 5: Executing the Method to Get Data Step 6: Parsing JSON and Writing the Contents to a CSV File Step 7: Loading CSV File to PostgreSQL Step 1: Installing the Python GA Library to Your Environment sudo pip install --upgrade google-api-python-client Before this step, please ensure the python programming environment is already installed and works fine. We will now start writing the script for downloading the data as a CSV file. Step 2: Importing the Required Libraries from apiclient.discovery import build from oauth2client.service_account import ServiceAccountCredentials Step 3: Initializing the Required Variables for OAuth Authentication credentials = ServiceAccountCredentials.from_json_keyfile_name(KEY_FILE_LOCATION, SCOPES) # Build the service object. analytics = build('analyticsreporting', 'v4', credentials=credentials) Replace the key file location and view ID with what we obtained in the first service creation step. View ids are the views from which you will be collecting the data. To get the view ID of a particular view that you have already configured, go to the admin section, click on the view that you need, and go to view settings. Step 4: Building the Required Objects credentials = ServiceAccountCredentials.from_json_keyfile_name(KEY_FILE_LOCATION, SCOPES)#Build the service object analytics = build('analyticsreporting', 'v4', credentials=credentials) Step 5: Executing the Method to Get Data In this step, you need to execute the method to get the data. The below query is for getting the number of users aggregated by country from the last 7 days. response = analytics.reports().batchGet(body={ 'reportRequests': [ { 'viewId': VIEW_ID, 'dateRanges': [{'startDate': '7daysAgo', 'endDate': 'today'}], 'metrics': [{'expression': 'ga:sessions'}], 'dimensions': [{'name': 'ga:country'}] }] } ).execute() Step 6: Parsing JSON and Writing the Contents to a CSV File import pandas as pd from pandas.io.json import json_normalize reports = response['reports'][0] columnHeader = reports['columnHeader']['dimensions'] metricHeader = reports['columnHeader']['metricHeader']['metricHeaderEntries'] columns = columnHeader for metric in metricHeader: columns.append(metric['name']) data = json_normalize(reports['data']['rows']) data_dimensions = pd.DataFrame(data['dimensions'].tolist()) data_metrics = pd.DataFrame(data['metrics'].tolist()) data_metrics = data_metrics.applymap(lambda x: x['values']) data_metrics = pd.DataFrame(data_metrics[0].tolist()) result = pd.concat([data_dimensions, data_metrics], axis=1, ignore_index=True) result.to_csv('reports.csv') Save the script and execute it. The result will be a CSV file with the following column: Id , ga:country, ga:sessions Step 7: Loading CSV File to PostgreSQL This file can be directly loaded to a PostgreSQL table using the below command. Please ensure the table is already created COPY sessions_tableFROM 'reports.csv' DELIMITER ',' CSV HEADER; The above command assumes you have already created a table named sessions_table. You now have your google analytics data in your PostgreSQL table. Now that we know how to do get the Google Analytics data using custom code, let’s look into the limitations of using this method. Limitations of using Manual ETL Scripts to Connect Google Analytics to PostgreSQL The above method requires you to write a lot of custom code. Google’s output JSON structure is a complex one and you may have to make changes to the above code according to the data you query from the API. This approach is fine for a one-off data load to PostgreSQL, but in a lot of cases, organizations need to do this periodically and merge the data point every day while handling duplicates. This will force you to write a very complex import tool just for Google Analytics. The above method addresses only one API that is available for Google Analytics. There are many other available APIs from Google analytics that provide different types of data. An example is a real-time API. All these APIs come with a different output JSON structure and the developers will need to write separate parsers. The APIs are rate limited which means the above approach will lead to errors if complex logic is not implemented to throttle the API calls. A solution to all the above problems is to use a completely managed ETL solution like LIKE.TG which provides a simple click and execute interface to move data from Google Analytics to PostgreSQL. Use Cases to transfer your Google Analytics 4 (GA4) data to Postgres There are several advantages to integrating Google Analytics 4 (GA4) data with Postgres. A few use cases are as follows: Advanced Analytics: With Postgres’ robust data processing features, you can extract insights from your Google Analytics 4 (GA4) data that are not feasible with Google Analytics 4 (GA4) alone. You can execute sophisticated queries and data analysis on your data. Data Consolidation: Syncing to Postgres enables you to centralize your data for a comprehensive picture of your operations and to build up a change data capturing procedure that ensures there are never any inconsistencies in your data again if you’re utilizing Google Analytics 4 (GA4) together with many other sources. Analysis of Historical Data: Historical data in Google Analytics 4 (GA4) is limited. Data sync with Postgres enables long-term data storage and longitudinal trend analysis. Compliance and Data Security: Strong data security protections are offered by Postgres. Syncing Google Analytics 4 (GA4) data with Postgres enables enhanced data governance and compliance management while guaranteeing the security of your data. Scalability: Growing enterprises with expanding Google Analytics 4 (GA4) data will find Postgres to be an appropriate choice since it can manage massive amounts of data without compromising speed. Machine Learning and Data Science: You may apply machine learning models to your data for predictive analytics, consumer segmentation, and other purposes if you have Google Analytics 4 (GA4) data in Postgres. Reporting and Visualization: Although Google Analytics 4 (GA4) offers reporting capabilities, more sophisticated business intelligence alternatives may be obtained by connecting to Postgres using data visualization tools like Tableau, PowerBI, and Looker (Google Data Studio). Airbyte can automatically convert your Google Analytics 4 (GA4) table to a Postgres table if needed. Conclusion This blog discusses the two methods you can deploy to connect Google Analytics to PostgreSQL seamlessly. While the custom method gives the user precise control over data, using automation tools like LIKE.TG can solve the problem easily. Visit our Website to Explore LIKE.TG While Google Analytics used to offer free website analytics, it’s crucial to remember that the program is currently built on a subscription basis. Presently, the free version is called Google Analytics 360, and it still offers insightful data on user behavior and website traffic. In addition to Google Analytics, LIKE.TG natively integrates with many other applications, including databases, marketing and sales applications, analytics applications, etc., ensuring that you have a reliable partner to move data to PostgreSQL at any point. Want to take LIKE.TG for a ride? Sign Up for a 14-day free trial and simplify your Data Integration process. Do check out the pricing details to understand which plan meets all your business needs. Tell us in the comments about your experience of connecting Google Analytics to PostgreSQL!
 Loading Data to Redshift: 4 Best Methods
Loading Data to Redshift: 4 Best Methods
Amazon Redshift is a petabyte-scale Cloud-based Data Warehouse service. It is optimized for datasets ranging from a hundred gigabytes to a petabyte can effectively analyze all your data by allowing you to leverage its seamless integration support for Business Intelligence tools Redshift offers a very flexible pay-as-you-use pricing model, which allows the customers to pay for the storage and the instance type they use. Increasingly, more and more businesses are choosing to adopt Redshift for their warehousing needs. In this article, you will gain information about one of the key aspects of building your Redshift Data Warehouse: Loading Data to Redshift. You will also gain a holistic understanding of Amazon Redshift, its key features, and the different methods for loading Data to Redshift. Read along to find out in-depth information about Loading Data to Redshift. Methods for Loading Data to Redshift There are multiple ways of loading data to Redshift from various sources. On a broad level, data loading mechanisms to Redshift can be categorized into the below methods: Method 1: Loading an Automated Data Pipeline Platform to Redshift Using LIKE.TG ’s No-code Data Pipeline LIKE.TG ’s Automated No Code Data Pipeline can help you move data from 150+ sourcesswiftly to Amazon Redshift. You can set up the Redshift Destination on the fly, as part of the Pipeline creation process, or independently. The ingested data is first staged in LIKE.TG ’s S3 bucket before it is batched and loaded to the Amazon Redshift Destination. LIKE.TG can also be used to perform smooth transitions to Redshift such as DynamoDB load data from Redshift and to load data from S3 to Redshift. LIKE.TG ’s fault-tolerant architecture will enrich and transform your data in a secure and consistent manner and load it to Redshift without any assistance from your side. You can entrust us with your data transfer process by both ETL and ELT processes to Redshift and enjoy a hassle-free experience. LIKE.TG Data focuses on two simple steps to get you started: Step 1: Authenticate Source Connect LIKE.TG Data with your desired data source in just a few clicks. You can choose from a variety of sources such as MongoDB, JIRA, Salesforce, Zendesk, Marketo, Google Analytics, Google Drive, etc., and a lot more. Step 2: Configure Amazon Redshift as the Destination You can carry out the following steps to configure Amazon Redshift as a Destination in LIKE.TG : Clickon the “DESTINATIONS”option in theAsset Palette. Clickthe “+ CREATE”option in theDestinations List View. On theAdd Destinationpage, selectthe Amazon Redshift option. In theConfigure your Amazon Redshift Destinationpage, specify the following: Destination Name, Database Cluster Identifier, Database Port, Database User, Database Password, Database Name, Database Schema. Clickthe Test Connectionoption to test connectivity with the Amazon Redshift warehouse. After the is successful, clickthe “SAVE DESTINATION” button. Here are more reasons to try LIKE.TG : Integrations: LIKE.TG ’s fault-tolerant Data Pipeline offers you a secure option to unify data from150+ sources(including 40+ free sources)and store it in Redshift or any other Data Warehouse of your choice. This way you can focus more on your key business activities and let LIKE.TG take full charge of the Data Transfer process. Schema Management:LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to yourRedshift schema. Quick Setup: LIKE.TG with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations. LIKE.TG Is Built To Scale:As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency. Live Support:The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls. With continuous Real-Time data movement, LIKE.TG allows you to assemble data from multiple data sources and seamlessly load it to Redshift with a no-code, easy-to-setup interface. Try our 14-day full-feature access free trial! Get Started with LIKE.TG for Free Seamlessly Replicate Data from 150+ Data Sources in minutes LIKE.TG Data, an AutomatedNo-code Data Pipeline, helps you load data to Amazon Redshift in real-time and provides you with a hassle-free experience. You can easily ingest data using LIKE.TG ’s Data Pipelines and replicate it to your Redshift warehouse without writing a single line of code. Get Started with LIKE.TG for Free LIKE.TG supports direct integrations of 150+ sources (including 40+ free sources) and its Data Mapping feature works continuously to replicate your data to Redshift and builds a single source of truth for your business. LIKE.TG takes full charge of the data transfer process, allowing you to focus your resources and time on other key business activities. Experience an entirely automated hassle-free process of loading data to Redshift. Try our 14-day full access free trial today! Method 2: Loading Data to Redshift using the Copy Command The Redshift COPY command is the standard way of loading bulk data TO Redshift. COPY command can use the following sources for loading data. DynamoDB Amazon S3 storage Amazon EMR cluster Other than specifying the locations of the files from where data has to be fetched, the COPY command can also use manifest files which have a list of file locations. It is recommended to use this approach since the COPY command supports the parallel operation and copying a list of small files will be faster than copying a large file. This is because, while loading data from multiple files, the workload is distributed among the nodes in the cluster. Download the Cheatsheet on How to Set Up High-performance ETL to Redshift Learn the best practices and considerations for setting up high-performance ETL to Redshift COPY command accepts several input file formats including CSV, JSON, AVRO, etc. It is possible to provide a column mapping file to configure which columns in the input files get written to specific Redshift columns. COPY command also has configurations to simple implicit data conversions. If nothing is specified the data types are converted automatically to Redshift target tables’ data type. The simplest COPY command for loading data from an S3 location to a Redshift target table named product_tgt1 will be as follows. A redshift table should be created beforehand for this to work. copy product_tgt1 from 's3://productdata/product_tgt/product_tgt1.txt' iam_role 'arn:aws:iam::<aws-account-id>:role/<role-name>' region 'us-east-2'; Method 3: Loading Data to Redshift using Insert Into Command Redshift’s INSERT INTO command is implemented based on the PostgreSQL. The simplest example of the INSERT INTO command for inserting four values into a table named employee_records is as follows. INSERT INTO employee_records(emp_id,department,designation,category) values(1,’admin’,’assistant’,’contract’); It can perform insertions based on the following input records. The above code snippet is an example of inserting single row input records with column names specified with the command. This means the column values have to be in the same order as the provided column names. An alternative to this command is the single row input record without specifying column names. In this case, the column values are always inserted into the first n columns. INSERT INTO command also supports multi-row inserts. The column values are provided with a list of records. This command can also be used to insert rows based on a query. In that case, the query should return the values to be inserted into the exact columns in the same order specified in the command. Even though the INSERT INTO command is very flexible, it can lead to surprising errors because of the implicit data type conversions. This command is also not suitable for the bulk insert of data. Method 4: Loading Data to Redshift using AWS Services AWS provides a set of utilities for loading data To Redshift from different sources. AWS Glue and AWS Data pipeline are two of the easiest to use services for loading data from AWS table. AWS Data Pipeline AWS data pipeline is a web service that offers extraction, transformation, and loading of data as a service. The power of the AWS data pipeline comes from Amazon’s elastic map-reduce platform. This relieves the users of the headache to implement a complex ETL framework and helps them focus on the actual business logic. To have a comprehensive knowledge of AWS Data Pipeline, you can also visit here. AWS Data pipeline offers a template activity called RedshiftCopyActivity that can be used to copy data from different kinds of sources to Redshift. RedshiftCopyActivity helps to copy data from the following sources. Amazon RDS Amazon EMR Amazon S3 storage RedshiftCopyActivity has different insert modes – KEEP EXISTING, OVERWRITE EXISTING, TRUNCATE, APPEND. KEEP EXISTING and OVERWRITE EXISTING considers the primary key and sort keys of Redshift and allows users to control whether to overwrite or keep the current rows if rows with the same primary keys are detected. AWS Glue AWS Glue is an ETL tool offered as a service by Amazon that uses an elastic spark backend to execute the jobs. Glue has the ability to discover new data whenever they come to the AWS ecosystem and store the metadata in catalogue tables.You can explore in detail the importance of AWS Glue from here. Internally Glue uses the COPY and UNLOAD command to accomplish copying data to Redshift. For executing a copying operation, users need to write a glue script in its own domain-specific language. Glue works based on dynamic frames. Before executing the copy activity, users need to create a dynamic frame from the data source. Assuming data is present in S3, this is done as follows. connection_options = {"paths": [ "s3://product_data/products_1", "s3://product_data/products_2"]} df = glueContext.create_dynamic_frame_from_options("s3_source", connection-options) The above command creates a dynamic frame from two S3 locations. This dynamic frame can then be used to execute a copy operation as follows. connection_options = { "dbtable": "redshift-target-table", "database": "redshift-target-database", "aws_iam_role": "arn:aws:iam::account-id:role/role-name" } glueContext.write_dynamic_frame.from_jdbc_conf( frame = s3_source, catalog_connection = "redshift-connection-name", connection_options = connection-options, redshift_tmp_dir = args["TempDir"]) The above method of writing custom scripts may seem a bit overwhelming at first. Glue can also auto-generate these scripts based on a web UI if the above configurations are known. Benefits of Loading Data to Redshift Some of the benefits of loading data to Redshift are as follows: 1) It offers significant Query Speed Upgrades Amazon’s Massively Parallel Processing allows BI tools that use the Redshift connector to process multiple queries across multiple nodes at the same time, reducing workloads. 2) It focuses on Ease of use and Accessibility MySQL (and other SQL-based systems) continue to be one of the most popular and user-friendly database management interfaces. Its simple query-based system facilitates platform adoption and acclimation. Instead of creating a completely new interface that would require significant resources and time to learn, Amazon chose to create a platform that works similarly to MySQL, and it has worked extremely well. 3) It provides fast Scaling with few Complications Redshift is a cloud-based application that is hosted directly on Amazon Web Services, the company’s existing cloud infrastructure. One of the most significant advantages this providesRedshift is a scalable architecture that can scale in seconds to meet changing storage requirements. 4) It keeps Costs relatively Low Amazon Web Services bills itself as a low-cost solution for businesses of all sizes. In line with the company’s positioning, Redshift offers a similar pricing model that provides greater flexibility while enabling businesses to keep a closer eye on their data warehousing costs. This pricing capability stems from the company’s cloud infrastructure and its ability to keep workloads to a minimum on the majority of nodes. 5) It gives you Robust Security Tools Massive data sets frequently contain sensitive data, and even if they do not, they contain critical information about their organisations. Redshift provides a variety of encryption and security tools to make warehouse security even easier. These all features make Redshift one of the best Data Warehouses to securely and efficiently load data in. A No-Code Data Pipeline such asLIKE.TG Data provides you with a smooth and hassle-free process for loading data to Redshift. Conclusion The above sections detail different ways of copying data to Redshift. The first two methods of COPY and INSERT INTO command use Redshift’s native ability, while the last two methods build abstraction layers over the native methods. Other than this, it is also possible to build custom ETL tools based on the Redshift native functionality. AWS’s own services have some limitations when it comes to data sources outside the AWS ecosystem. All of this comes at the cost of time and precious engineering resources. Visit our Website to Explore LIKE.TG LIKE.TG Datais the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources such as PostgreSQL, MySQL, and MS SQL Server, we help you not only export data from sources load data to the destinations but also transform enrich your data, make it analysis-ready. Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand. You may also have a look at the amazing price, which will assist you in selecting the best plan for your requirements. Share your experience of understanding Loading data to Redshift in the comment section below! We would love to hear your thoughts.
 SQS to S3: Move Data Using AWS Lambda and AWS Firehose
SQS to S3: Move Data Using AWS Lambda and AWS Firehose
AWS Simple Queue Service is a completely managed message queue service offered by Amazon. Queue services are typically used to decouple systems and services in the microservice architecture. In that sense, SQS is a software-as-a-service alternative for queue systems like Kafka, RabbitMQ, etc. AWS S3 or Simple Storage Service is another software-as-a-service offered by Amazon. S3 is a complete solution for any kind of storage needs for up to 5 terabytes. SQS and S3 form an integral part of applications exploiting cloud-based microservices architecture and it is very common to have a requirement of transferring messages from SQS to S3 to keep a historical record of everything that is coming through the queue. This post is about the methods to accomplish this transfer. What is SQS? SQS frees the developers from the complexity and effort associated with developing, maintaining, and operating a highly reliable queue layer. It helps to send, receive and store messages between software systems. The standard size of messages is capped at 256 KBs. But with the extended AWS SDK, a message size of up to 2 GB is supported. Messages greater than 256KB in size will by default be using S3 as the internal storage. One of the greatest advantages of using SQS instead of traditional queue systems like Kafka is that it allows virtually unlimited scaling without the customer having to worry about capacity planning or pre-provisioning. AWS offers a very flexible pricing plan for SQS based on the pay-as-you-go model and it provides significant cost savings when compared to the always-on model. Behind the scenes, SQS messages are stored in distributed SQS servers for redundancy. SQS offers two types of queues – A standard queue and a FIFO queue. Standard queue offers at least one guarantee which means that occasionally duplicate messages might reach the receiver. The FIFO queue is designed for applications where the order of the events and uniqueness of the messages is critical. It provides an exactly-once guarantee. SQS offers a dead-letter queue for routing problematic or erroneous messages that can not be processed in normal conditions. Amazon offers a standard queue at .40$ per 1 million requests and the FIFO queue at .50$ per 1 million requests. The total cost of ownership will also include data storage costs. Solve your data integration problems with LIKE.TG ’s reliable, no-code, automated pipelines with 150+ connectors.Get your free trial right away! What is S3? AWS S3 is a completely managed object storage service that can be used for a variety of use cases like hosting data, backup and archiving, data warehousing, etc. Amazon handles all operation and maintenance activities related to scaling, provisioning, etc. and the customers only need to pay for the storage that they use. It offers fine-grained access controls to meet any kind of organizational and business compliance requirements through an easy-to-use management user interface. S3 also supports analytics through the use of AWS Athena and AWS Redshift Spectrum which enables users to execute SQL scripts on the stored data. S3 data is encrypted by default at rest. S3 achieves state-of-the-art availability by storing the data across distributed servers. A caveat to this approach is that there is normally a propagation delay and S3 only guarantees eventual consistency. That said, the writes are atomic; which means at any point, the API will return either the old data or new data and never a corrupted response. Conceptually S3 is organized as buckets and objects. A bucket is the highest level S3 namespace and acts as a container for storing objects. They have a critical role in access control and usage reporting is always aggregated at the bucket level. An object is the fundamental storage entity and consists of the actual object as well as the metadata. An object is uniquely identified by a unique key and a version identifier. Customers can choose the AWS regions in which their buckets need to be located according to their cost and latency requirements. A point to note here is that objects do not support locking and if two PUTs come at the same time, the request with the latest timestamp will win. This means if there is concurrent access, users will have to implement some kind of locking mechanism on their own. Steps to Load data fromSQS to S3 The most straightforward approach to transfer data from SQS to S3 is to use standard AWS services like Lambda functions and AWS firehose. AWS Lambda functions are serverless functions that allow users to execute arbitrary logic using amazon’s infrastructure. These functions can be triggered based on specific events or scheduled based on required intervals. It is pretty straightforward to write a Lambda function to execute based on messages from SQS and write it to S3. The caveat is that this will create an S3 object for every message that is received and this is not always the ideal outcome. To create files in S3 after buffering the SQS messages for a fixed interval of time, there are two approaches for SQS to S3 data transfer: Through a Scheduled Lambda FunctionUsing a Triggered Lambda Function and AWS Firehose 1) Through a Scheduled Lambda Function A scheduled Lambda function for SQS to S3 transfer is executed in predefined intervals and can consume all the SQS messages that were produced during that specific interval. Once it processes all the messages, it can create a multi-part S3 upload using API calls. To schedule a Lambda function that transfers data from SQS to S3, execute the below steps. Sign in to the AWS console and go to the Lambda console.Choose to create a function.For the execution role, select create a new execution role with Lambda permissions.Choose to use a blueprint. Blueprints are prototype code snippets that are already implemented to provide examples for users. Search for hello-world blueprint in the search box and choose it. Click create function. On the next page, click to add a trigger. In the trigger search menu, search and select CloudWatch events. CloudWatch events are used to schedule Lambda functions.Click create a new rule and select rule type as scheduled expression. Scheduled expression takes a Cron expression. You can enter a valid Cron expression corresponding to your execution strategy. The Lambda function will contain code to access the SQS and to execute a multi-part upload to S3. S3 mandates that all single file uploads greater than 500 MB should be multipart.Choose create a function to activate the Lambda function.Once this is configured, AWS CloudWatch will generate events according to the cron expression, schedule, and trigger the Lambda function. A problem with this approach is that Lambda functions have an execution time ceiling of 15 minutes and a usable memory ceiling of 3008 MB. If there are a large number of SQS events, you can run out of time and memory limits leading to dropping messages. 2) Using a Triggered Lambda Function and AWS Firehose A deterrent to using a triggered Lambda function to move data from SQS to S3 was that it would create an S3 object per message leading to a large number of destination files. A workaround to avoid this problem is to use a buffered delivery stream that can write to S3 in predefined intervals. This approach involves the following broad set of steps. Step 1: Create a triggered Lambda function To create a triggered Lambda function for SQS to S3 data transfer, follow the same steps from the first approach. Instead of selecting a schedule expression select triggers. Amazon will provide you with a list of possible triggers. Select the SQS trigger and click create function. In the Lambda function write a custom code to redirect the SQS messages to Kinesis Firehose Delivery Stream. Step 2: Create a Firehose Delivery Stream To create a delivery stream, go to the AWS console and select the Kinesis Data Firehose Console. Choose the destination as S3. In the configuration options, you will be presented with options to select the buffer size and buffer interval. Buffer size is the amount of data up to which kinesis firehose will buffer the messages before writing to S3 as an object. You can have any value from 1 MB to 128 MB here.Buffer interval is the amount of time up to which the firehose will wait before it writes to S3. You can select any value from 60 seconds to 900 seconds here. After selecting the buffer size and buffer interval, you can leave the other parameters as default and click on create. That completes the pipeline to transfer data from SQS to S3. The main limitation of this approach is that the user does not have close control over when to write to S3 beyond the buffer interval and buffer size limits imposed by Amazon. These limits are not always practical in real scenarios. What Makes Your Data Integration Experience With LIKE.TG Unique? These are some benefits of having LIKE.TG Data as your Data Automation Partner: Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Auto Schema Mapping: LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to the S3 schema.Integrate With Custom Sources:LIKE.TG allows businesses to move data from 100+ Data Sources straight to thier desired destination.Quick Setup: LIKE.TG with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations using just 3 simple steps.LIKE.TG Is Built To Scale: As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency.Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls. With continuous real-time data movement, ETL your data seamlessly from your data sources to a destination of your choice with LIKE.TG ’s easy-to-setup and No-code interface. Try our14-dayfull access free trial! Explore LIKE.TG Platform With A 14-Day Free Trial SQS to S3: Limitations of the Custom-Code Approach Both the approaches mentioned for SQS to S3 data transfer use AWS-provided functions. An obvious advantage here is that you can implement the whole pipeline staying inside the AWS ecosystem. But these approaches have a number of limitations as mentioned below. Both approaches require a lot of custom coding and knowledge of AWS proprietary configurations. Some of these configurations are very confusing and can lead to a significant amount of time and effort expense.AWS imposes multiple limits for execution time, run time memory, and storage memory in case of the services that we used to accomplish this transfer. This is not always practical in real scenarios. Conclusion In this blog, you learned how to move data from SQS to S3 using AWS Lambda and AWS Firehouse. You also went through the limitations of using custom code for SQS to S3 data migration. The AWS Lambda and Firehouse-based approach for loading data from SQS to S3 will consume a significant amount of time and resources. Moreover, it will be an error-prone method and you will be required to debug and maintain the data transfer process regularly. LIKE.TG Data provides an Automated No-code Data Pipeline that empowers you to overcome the above-mentioned limitations. LIKE.TG caters to 100+ data sources (40+ free sources). Furthermore, LIKE.TG ’s fault-tolerant architecture ensures a consistent and secure transfer of your data to a Data Warehouse. Using LIKE.TG will make your life easier and make Data Transfer hassle-free. Learn more about LIKE.TG Share your experience of loading data from SQS to S3 in the comment section below.
 HubSpot to Snowflake Integration: 2 Easy Methods
HubSpot to Snowflake Integration: 2 Easy Methods
The advent of the internet and the cloud has paved the way for SaaS companies like Shopify to simplify the cumbersome task of setting up and running a business online. The businesses that use Shopify have crucial data about their customers, products, catalogs, orders, etc. within Shopify and would often need to extract this data out of Shopify into a central database and combine this with their advertising, ads, etc. to derive meaningful insights. PostgreSQL has emerged as a top ORDBMS (object-relational database management system) that is highly extensible with technical standards compliance. PostgreSQL’s ease of set up and
 Shopify to BigQuery: 2 Easy Methods
Shopify to BigQuery: 2 Easy Methods
You have your complete E-Commerce store set up on Shopify. You Collect data on the orders placed, Carts abandoned, Products viewed, and so on. You now want to move all of this data on Shopify to a robust Data Warehouse such as Google BigQuery so that you can combine this information with data from many other sources and gain deep insights. Well, you have landed on the right blog. This blog will discuss 2 step-by-step methods for moving data from Shopify to BigQuery for analytics. First, it will provide a brief introduction to Shopify and
 Amazon S3 to Redshift: 3 Easy Methods
Amazon S3 to Redshift: 3 Easy Methods
You have your complete E-Commerce store set up on Shopify. You Collect data on the orders placed, Carts abandoned, Products viewed, and so on. You now want to move all of this data on Shopify to a robust Data Warehouse such as Google BigQuery so that you can combine this information with data from many other sources and gain deep insights. Well, you have landed on the right blog. This blog will discuss 2 step-by-step methods for moving data from Shopify to BigQuery for analytics. First, it will provide a brief introduction to Shopify and
 The Best Data Pipeline Tools List for 2024
The Best Data Pipeline Tools List for 2024
Businesses today generate massive amounts of data. This data is scattered across different systems used by the business: Cloud Applications, databases, SDKs, etc. To gain valuable insight from this data, deep analysis is required. As a first step, companies would want to move this data to a single location for easy access and seamless analysis. This article introduces you to Data Pipeline Tools and the factors that drive a Data Pipeline Tools Decision. It also provides the difference between Batch vs. Real-Time Data Pipeline, Open Source vs. Proprietary Data Pipeline, and On-premise vs. Cloud-native Data Pipeline Tools. Before we dive into the details, here is a snapshot of what this post covers: What is a Data Pipeline Tool? Dealing with data can be tricky. To be able to get real insights from data, you would need to perform ETL: Extract data from multiple data sources that matter to you. Transform and enrich this data to make it analysis-ready. Load this data to a single source of truth more often a Data Lake or Data Warehouse. Each of these steps can be done manually. Alternatively, each of these steps can be automated using separate software tools too. However, during the process, many things can break. The code can throw errors, data can go missing, incorrect/inconsistent data can be loaded, and so on. The bottlenecks and blockers are limitless. Often, a Data Pipeline tool is used to automate this process end-to-end efficiently, reliably, and securely. Data Pipeline software has many advantages, including the guarantee of a consistent and effortless migration from various data sources to a destination, often a Data Lake or Data Warehouse. 1000+ data teams trust LIKE.TG ’s robust and reliable platform to replicate data from 150+ plug-and-play connectors.START A 14-DAY FREE TRIAL! Types of Data Pipeline Tools Depending on the purpose, different types of Data Pipeline tools are available. The popular types are as follows: Batch vs Real-time Data Pipeline Tools Open source vs Proprietary Data Pipeline Tools On-premise vs Cloud-native Data Pipeline Tools 1) Batch vs. Real-time Data Pipeline Tools Batch Data Pipeline tools allow you to move data, usually a very large volume, at a regular interval or batches. This comes at the expense of real-time operation. More often than not, these type of tools is used for on-premise data sources or in cases where real-time processing can constrain regular business operation due to limited resources. Some of the famous Batch Data Pipeline tools are as follows: Informatica PowerCenter IBM InfoSphere DataStage Talend Pentaho The real-time ETL tools are optimized to process data in real-time. Hence, these are perfect if you are looking to have analysis ready at your fingertips day in-day out. These tools also work well if you are looking to extract data from a streaming source, e.g. the data from user interactions that happen on your website/mobile application. Some of the famous real-time data pipeline tools are as follows: LIKE.TG Data Confluent Estuary Flow StreamSets 2) Open Source vs. Proprietary Data Pipeline Tools Open Source means the underlying technology of the tool is publicly available and therefore needs customization for every use case. This type of Data Pipeline tool is free or charges a very nominal price. This also means you would need the required expertise to develop and extend its functionality as needed. Some of the known Open Source Data Pipeline tools are: Talend Apache Kafka Apache Airflow The Proprietary Data Pipeline tools are tailored as per specific business use, therefore require no customization and expertise for maintenance on the user’s part. They mostly work out of the box. Here are some of the best Proprietary Data Pipeline tools that you should explore: LIKE.TG Data Blendo Fly Data 3) On-premises vs. Cloud-native Data Pipeline Tools Previously, businesses had all their data stored in On-premise systems. Hence, a Data Lake or Data Warehouse also had to be set up On-premise. These Data Pipeline tools clearly offer better security as they are deployed on the customer’s local infrastructure. Some of the platforms that support On-premise Data Pipelines are: Informatica Powercenter Talend Oracle Data Integrator Cloud-native Data Pipeline tools allow the transfer and processing of Cloud-based data to Data Warehouses hosted in the cloud. Here the vendor hosts the Data Pipeline allowing the customer to save resources on infrastructure. Cloud-based service providers put a heavy focus on security as well. The platforms that support Cloud Data Pipelines are as follows: LIKE.TG Data Blendo Confluent The choice of a Data Pipeline that would suit you is based on many factors unique to your business. Let us look at some criteria that might help you further narrow down your choice of Data Pipeline Tool. Factors that Drive Data Pipeline Tool Decision With so many Data Pipeline tools available in the market, one should consider a couple of factors while selecting the best-suited one as per the need. Easy Data Replication: The tool you choose should allow you to intuitively build a pipeline and set up your infrastructure in minimal time. Maintenance Overhead: The tool should have minimal overhead and work out of the box. Data Sources Supported: It should allow you to connect to numerous and various data sources. You should also consider support for those sources you may need in the future. Data Reliability: It should transfer and load data without error or dropped packet. Realtime Data Availability: Depending on your use case, decide if you need data in real-time or in batches will be just fine. Customer Support: Any issue while using the tool should be solved quickly and for that choose the one offering the most responsive and knowledgeable customer sources Scalability: Check whether the data pipeline tool can handle your current and future data volume needs. Security: Access if the tool you are choosing can provide encryption and other necessary regulations for data protection. Documentation: Look out if the tool has proper documentation or community to help when any need for troubleshooting arises. Cost: Check the costs of license and maintenance of the data pipeline tool that you are choosing, along with its features to ensure that it is cost-effective for you. Here is a list of use cases for the different Data Pipeline Tools mentioned in this article: LIKE.TG , No-code Data Pipeline Solution LIKE.TG is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines from 150+ sources that are flexible to your needs. For the rare times things do go wrong, LIKE.TG ensures zero data loss. To find the root cause of an issue, LIKE.TG also lets you monitor your workflow so that you can address the issue before it derails the entire workflow. Add 24*7 customer support to the list, and you get a reliable tool that puts you at the wheel with greater visibility. Check LIKE.TG ’s in-depth documentation to learn more. LIKE.TG offers a simple, and transparent pricing model. LIKE.TG has 3 usage-based pricing plans starting with a free tier, where you can ingest upto 1 million records. What makes LIKE.TG amazing: Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer. Schema Management: LIKE.TG can automatically detect the schema of the incoming data and maps it to the destination schema. Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends. LIKE.TG was the most mature Extract and Load solution available, along with Fivetran and Stitch but it had better customer service and attractive pricing. Switching to a Modern Data Stack with LIKE.TG as our go-to pipeline solution has allowed us to boost team collaboration and improve data reliability, and with that, the trust of our stakeholders on the data we serve. – Juan Ramos, Analytics Engineer, Ebury Check out how LIKE.TG empowered Ebury to build reliable data products here. Sign up here for a 14-Day Free Trial! Business Challenges That Data Pipelines Mitigates: Data Pipelines face the following business challenges and overcome them while serving your organization: Operational Efficiency It is difficult to orchestrate and manage complex data workflows. You can improve the operational efficiency of your workflow using data pipelines through automated workflow orchestration tools. Real-time Decision-Making Sometimes there is a delay in decision-making because of traditional batch processing. Data pipelines enable real-time data processing and speed up an organization’s decision-making. Scalability Traditional systems cannot handle large volumes of data, which can strain their performance. Data pipelines that are cloud-based provide scalable infrastructure and optimized performance. Data Integration The organizations usually have data scattered across various sources, which poses challenges. Data pipelines, through the ETL process, can ensure the consolidation of data in a central repository. Conclusion The article introduced you to Data Pipeline Tools and the factors that drive Data Pipeline Tools decisions. It also provided the difference between Batch vs. Real-Time Data Pipeline, Open Source vs. Proprietary Data Pipeline, and On-premise vs. Cloud-native Data Pipeline Tools. Now you can also read about LIKE.TG ’s Inflight Transformation feature and know how it improves your ELT data pipeline productivity. A Data Pipeline is the mechanism by which ETL processes occur. Now you can learn more about the best ETL tools that simplify the ETL process. Visit our Website to Explore LIKE.TG Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand. Share your experience of finding the Best Data Pipeline Tools in the comments section below!
 Shopify to Redshift: 2 Easy Methods
Shopify to Redshift: 2 Easy Methods
Software As A Service offerings like Shopify has revolutionized the way businesses step up their Sales channels. Shopify provides a complete set of tools to aid in setting up an e-commerce platform in a matter of a few clicks. Shopify comes bundles with all the configurations to support a variety of payment gateways and customizable online shop views. Bundles with this package are also the ability to run analysis and aggregation over the customer data collected through Shopify images. Even with all these built-in Shopify capabilities, organizations sometimes need to import the data from Shopify to their Data Warehouse since that allows them to derive meaningful insights by combining the Shopify data with their organization data. Doing this also means they get to use the full power of a Data Warehouse rather than being limited to the built-in functionalities of Shopify Analytics. This post is about the methods in which data can be loaded from Shopify to Redshift, one of the most popular cloud-based data warehouse. Solve your data replication problems with LIKE.TG ’s reliable, no-code, automated pipelines with 150+ connectors.Get your free trial right away! Shopify to Redshift: Approaches to Move Data This blog covers two methods for migrating data from Shopify to Redshift: Method 1: Using Shopify APIs to connect Shopify to Redshift Making use of Shopify APIs to connect with Redshift is one such way. Shopify provides multiple APIs such as Billing, Customer, Inventory, etc., and can be accessed through its RESTful endpoints. This method makes use of custom code to connect with Shopify APIs and uses it to connect Shopify to Redshift. Method 2: Using LIKE.TG Data, a No-code Data Pipeline to Connect Shopify to Redshift Get started with LIKE.TG for free A fully managed,No-code Data Pipeline platformlikeLIKE.TG Data, helps you load data from Shopify (among 40+ Free Sources) to Redshift in real-time, in an effortless manner. LIKE.TG with its minimal learning curve can be set up in a matter of minutes making the users ready to load data without compromising performance. Its strong integration with various sources such as Databases, Files, Analytics Engine, etc gives users the flexibility to bring in data of all different kinds in a way that’s as smooth as possible, without having to write a single line of code. It helps transfer data fromShopifyto a destination of your choice forfree. Get started with LIKE.TG ! Sign up here for a 14-day free trial! Methods to connect Shopify to Redshift There are multiple methods that can be used to connect Shopify to Redshift and load data easily: Method 1: Using Shopify APIs to connect Shopify to RedshiftMethod 2: Using LIKE.TG Data, a No-code Data Pipeline to Connect Shopify to Redshift Method 1: Using Shopify APIs to connect Shopify to Redshift Since Redshift supports loading data to tables using CSV, the most straightforward way to accomplish this move is to use the CSV export feature of Shopify Admin. But this is not always practical since this is a manual process and is not suitable for the kind of frequent sync that typical organizations need. We will focus on the basics of accomplishing this in a programmatic way which is much better suited for typical requirements. Shopify provides a number of APIs to access the Product, Customer, and Sales data. For this exercise, we will use the Shopify Private App feature. A Private App is an app built to access only the data of a specific Shopify Store. To create a Private App script, we first need to create a username and password in the Shopify Admin. Once you have generated the credentials, you can proceed to access the APIs. We will use the product API for reference in this post. Use the below snippet of code to retrieve the details of all the products in the specified Shopify store. curl --user shopify_app_user:shopify_app_password GET /admin/api/2019-10/products.json?limit=100 The important parameter here is the Limit parameter. This field is there because the API is paginated and it defaults to 50 results in case the Limit parameter is not provided. The maximum pagination limit is 250 results per second. To access the full data, Developers need to buffer the id of the last item in the previous request and use that to form the next curl request. The next curl request would look like as below. curl --user shopify_app_user:shopify_app_password GET /admin/api/2019-10/products.json? limit=100since_id=632910392 -o products.json You will need a loop to execute this. From the above steps, you will have a set of JSON files that should be imported to Redshift to complete our objective. Fortunately, Redshift provides a COPY command which works well with JSON data. Let’s create a Redshift table before we export the data. create table products( product_id varchar(25) NOT NULL, type varchar(25) NOT NULL, vendor varchar(25) NOT NULL, handle varchar(25) NOT NULL, published_scope varchar(25) NOT NULL ) Once the table is created, we can use the COPY command to load the data. Before copying ensure that the JSON files are loaded into an S3 bucket since we will be using S3 as the source for COPY command. Assuming data is already in S3, let’s proceed to the actual COPY command. The challenge here is that the Shopify API result JSON is a very complex nested JSON that has a large number of details. To map the appropriate keys to Redshift values, we will need a json_path file that Redshift uses to map fields in JSON to the Redshift table. The command will look as below. copy products from ‘s3://products_bucket/products.json’ iam_role ‘arn:aws:iam:0123456789012:role/MyRedshiftRole' json ‘s3://products_bucket/products_json_path.json’ The json_path file for the above command will be as below. { "jsonpaths": [ "$['id']", "$['product_type']", "$[‘vendor’]", "$[‘handle’]", "$[‘published_scope’]" ] } This is how you can connect Shopify to Redshift. Please note that this was a simple example and oversimplifies many of the actual pitfalls in the COPY process from Shopify to Redshift. Limitations of migrating data using Shopify APIs The Developer needs to implement a logic to accommodate the pagination that is part of the API results.Shopify APIs are rate limited. The requests are throttled based on a Leaky Bucket algorithm with a bucket size of 40 and 2 requests per second leak in case of admin APIs. So your custom script will need a logic to handle this limit in case your data volume is high.In case you need to Clean, Transform, Filter data before loading it to the Warehouse, you will need to build additional code to achieve this.The above approach works for a one-off load but if frequent sync which also handles duplicates is needed, additional logic needs to be developed using a Redshift Staging Table.In case you want to copy details that are inside the nested JSON structure or arrays in Shopify format, the json_path file development will take some development time. Method 2: Using LIKE.TG Data, a No-code Data Pipeline to Connect Shopify to Redshift LIKE.TG Data,a No-code Data Pipeline can help you move data from 100+ Data Sources including Shopify (among 40+ Free sources) swiftly to Redshift. LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. It helps transfer data fromShopifyto a destination of your choice forfree. Steps to use LIKE.TG Data: LIKE.TG Data focuses on two simple steps to get you started: Configure Source:Connect LIKE.TG Data with Shopify by simply providing the API key and Pipeline name. IntegrateData:Load data from Shopify to Redshift by simply providing your Redshift database credentials. Enter a name for your database, the host and port number for your Redshift database and connect in a matter of minutes. Advantages of using LIKE.TG Data Platform: Real-Time Data Export:LIKE.TG with its strong integration with 100+ sources, allows you to transfer data quickly efficiently. This ensures efficient utilization of bandwidth on both ends.Live Support:The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.Schema Management:LIKE.TG takes away the tedious task of schema management automatically detects schema of incoming data and maps it to the destination schema.Minimal Learning:LIKE.TG with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Live Monitoring: LIKE.TG allows you to monitor the data flow so you can check where your data is at a particular point in time. About Shopify Shopify is a powerful e-commerce platform designed to allow people or businesses to sell their offerings/products online. Shopify helps you set up an online store and also offers a Point Of Sale (POS) to sell the products in person. Shopify provides you with Payment Gateways, Customer Engagement techniques, Marketing, and even Shipping facilities to help you get started. Various product or services that you can sell on the Shopify: Physical Products:Shopify allows you to perform the door-step delivery of the products you’ve manufactured that can be door-shipped to the customer. These include anything like Printed Mugs/T-Shirt, Jewellery, Gifts, etc.Digital Products:Digital Products can include E-Books, Audios, Course Material, etc.Services and Consultation:If you’re providing services like Life Consultation, Home-Cooked delicacies, Event Planning, or anything else, Shopify has got you covered.Memberships:Various memberships such as Gym memberships, Yoga-classes membership, Event Membership, etc. can be sold to the customers.Experiences:Event-based experiences like Adventurous Sports and Travel, Mountain Trekking, Wine Tasting, events, and hands-on workshops. You can use Shopify to sell tickets for these experiences as well.Rentals:If you’re running rental services like Apartment rentals, rental Taxis, or Gadgets, you can use Shopify to create Ads and engage with the customer.Classes:Online studies, Fitness classes can be advertised here. Shopify allows you to analyze Trends and Customer Interaction on their platform. However, for advanced Analytics, you may need to store the data into some Database or Data Warehouse to perform in-depth Analytics and then move towards a Visualization tool to create appealing reports that can demonstrate these Trends and Market positioning. For further information on Shopify, you can check theofficial site here. About Redshift Redshiftis a columnar Data Warehouse managed by Amazon Web Services (AWS). It is designed to run complex Analytical problems in a cost-efficient manner. It can store petabyte-scale data and enable fast analysis. Redshift’s completely managed warehouse setup, combined with its powerful MPP (Massively Parallel Processing) have made it one of the most famous Cloud Data Warehouse options among modern businesses.You can read more about the features of Redshift here. Conclusion In this blog, you were introduced to the key features of Shopify and Amazon Redshift. You learned about two methods to connect Shopify to Redshift. The first method is connecting using Shopify API. However, you explored some of the limitations of this manual method. Hence, an easier alternative, LIKE.TG Data was introduced to you to overcome the challenges faced by previous methods. You can seamlessly connect Shopify to Redshift with LIKE.TG for free. visit our website to explore LIKE.TG Want to try LIKE.TG ? sign up for a 14-day free trialand experience the feature-rich LIKE.TG suite first hand. Have a look at our unbeatablepricing, which will help you choose the right plan for you. What are your thoughts on moving data from Shopify to Redshift? Let us know in the comments.
 Data Automation: Conceptualizing Industry-driven Use Cases
Data Automation: Conceptualizing Industry-driven Use Cases
As the data automation industry goes under a series of transformations, thanks to new strategic autonomous tools at our disposal, we now see a shift in how enterprises operate, cultivate, and sell value-driven services. At the same time, product-led growth paves the way for a productivity-driven startup ecosystem for better outcomes for every stakeholder.So, as one would explain, data automation is an autonomous process to collect, transfigure, or store data. Data automation technologies are in the use to execute time-consuming tasks that are recurring and replaceable to increase efficiency and minimize cost. Innovative use of data automation can enable enterprises to provide a superior user experience, inspired by custom and innovative use to cater to pressure points in the customer lifecycle. To cut a long story short, data automation can brush up user experience and drive better outcomes. In this article, we will talk about how data automation and its productivity-led use cases are transforming industries worldwide. We will discuss how data automation improves user experience and at the same time drive better business outcomes. Why Data Automation? Data automation has been transforming the way work gets done. Automation has helped companies empower teams by increasing productivity and nudging data transfer passivity. By automating bureaucratic activities from enterprises across vertices, we increase productivity, revenue, and customer satisfaction — quicker than before. Today, data automation has gained enough momentum that you just simply can’t execute without it. As one would expect, data automation has come with its own unique sets of challenges. But it’s the skill lag and race to save cost that contradicts and creates major discussion in the data industry today. Some market insights are as follows: A 2017 McKinsey report says, “half of today’s work activities could be automated by the end of 2055” — Cost reduction is prioritized. A 2017 Unit4 study revealed, “office workers spent 69 days in a year on administrative tasks, costing companies $5 trillion a year” — a justification to automate. And another research done by McKinsey estimated its outcome by surveying 1500 executives across industries and regions, out of which 66% of respondents believed that “addressing potential skills gaps related to automation/digitization was a top-ten priority” — data literacy is crucial in a data-driven environment. What is Data Warehouse Automation? A data warehouse is a single source of data truth, it works as a centralized repository for data generated from multiple sources. Each set of data has its unique use cases. The stored data helps companies generate business insights that are data predictive to help mitigate early signs of market nudges. Using Data Warehouse Automation (DWA) we automate data flow, from third-party sources to the data warehouses such as Redshift, Snowflake, and BigQuery. But shifting trends tell us another story — a shift in reverse. We have seen an increased demand for data-enriching applications like LIKE.TG Activate — to transfer the data from data warehouses to CRMs like Salesforce and HubSpot. Nevertheless, an agile data warehouse automation solution with a unique design, quick deployment settings, and no-code stock experience will lead its way. Let’s list out some of the benefits: Data Warehouse Automation solutions provide real-time, source to destination, ingestion, and update services. Automated and continuous refinements facilitate better business outcomes by simplifying data warehouse projects. Automated ETL processes eliminate any reoccurring steps through auto-mapping and job scheduling. Easy-to-use user interfaces and no-code platforms are enhancing user experience. Empower Success Teams With Customer-data Analytics Using LIKE.TG Activate LIKE.TG Activate helps you unify directly transfer data from data warehouses and other SaaS Product Analytics platforms like Amplitude, to CRMs such as Salesforce HubSpot, in a hassle-free automated manner. LIKE.TG Activate manages automates the process of not only loading data from your desired source but also enrich transform data into an analysis-ready format — without having to write a single line of code. LIKE.TG Activate takes care of pre-processing data needs and allows you to focus on key business activities, to draw compelling insights into your product’s performance, customer journey, high-quality leads, and customer retention through a personalized experience. Check out what makes LIKE.TG Activate amazing. Real-Time Data Transfer: LIKE.TG Activate, with its strong integration with 100+ sources, allows you to transfer data quickly efficiently. This ensures efficient utilization of bandwidth on both ends.Secure: LIKE.TG Activate has a fault-tolerant architecture that ensures data is handled safely and cautiously with zero data loss.Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.Tremendous Connector Availability: LIKE.TG Activate houses a diverse set of connectors that authorize you to bring data in from multiple data sources such as Google Analytics, Amplitude, Jira, and Oracle. And even data-warehouses such as Redshift and Snowflake are in an integrated and analysis-ready format. Live Support:The LIKE.TG Activate team is available round the clock to extend exceptional support to its customers through chat, email, and support calls. Get Customer-Centric with LIKE.TG Activate today!Sign up herefor exclusive early access into Activate! Customer Centricity Benefiting From Data Automation Today’s enterprises prefer tools that help customer-facing staff achieve greater success. Assisting customers on every twist and turn with unique use cases and touchpoints is now the name of the game. In return, the user touchpoint data is analyzed, to better engage customer-facing staff. Data automation makes customer data actionable. As data is available for the teams to explore, now companies can offer users competent customer service, inspired by unique personalized experiences. A train of thought: Focusing on everyday data requests from sales, customer success, and support teams, we can ensure success and start building a sophisticated CRM-centric data automation technology. Enriching the CRM software with simple data requests from teams mentioned above, can, in fact, make all the difference. Customer and Data Analytics Enabling Competitive Advantage Here, data automation has a special role to play. The art and science of data analytics are entangled with high-quality data collection and transformation abilities. Moving lightyears ahead from survey-based predictive analytics procedures, we now have entered a transition period, towards data-driven predictive insights and analytics. Thanks to better analytics, we can better predict user behavior, build cross-functional teams, minimize user churn rate, and focus first on the use cases that drive quick value. Four Use Cases Disrupting Legacy Operations Today 1. X-Analytics We can’t limit today’s autonomous tools to their primitive use cases as modern organizations generate data that is both unstructured and structured. Setting the COVID-19 pandemic an example of X-Analytics’s early use case: X-Analytics helped medical and public health experts by analyzing terabytes of data in the form of videos, research papers, social media posts, and clinical trials data. 2. Decision Intelligence Decision intelligence helps companies gain quick, actionable insights using customer/product data. Decision intelligence can amplify user experience and improve operations within the companies. 3. Blockchain in Data Analytics Smart contracts, with the normalization of blockchain technology, have evolved. Smart contracts increase transparency, data quality, and productivity. For instance, a process in a smart contract is initiated only when certain predetermined conditions are met. The process is designed to remove any bottlenecks that might come in between while officializing an agreement. 4. Augmented Data Management: As the global service industry inclines towards outsourcing the data storage and management needs, getting insights will become more complicated and time-consuming. Using AI and ML to automate lackluster tasks can reduce manual data management tasks by 45%. Data Automation is Changing the Way Work Gets Done Changing user behavior and customer buying trends are altering market realities today. At the same time, the democratization of data within organizations has enabled customer-facing staff to generate better results. Now, teams are encouraged, by design, to take advantage of data, to make compelling, data-driven decisions. Today, high-quality data is an integral part of a robust sales and marketing flywheel. Hence, keeping an eye on the future, treating relationships like partnerships and not just one-time transactional tedium, generates better results. Conclusion Alas, the time has come to say goodbye to our indulgence in recurring data transfer customs, as we embrace change happening in front of our eyes. Today, data automation has cocooned out of its early use cases and has aww-wittingly blossomed to benefit roles that are, in practice, the first touchpoint in any customers’ life cycle. And what about a startup’s journey to fully calibrate the product’s offering — how can we forget!? Today’s data industry has fallen sick of unstructured data silos, and wants an unhindered flow of analytics-ready data to facilitate business decisions– small or big, doesn’t matter. Now, with LIKE.TG Activate, directly transfer data from data warehouses such as Snowflake or any other SaaS application to CRMs like HubSpot, Salesforce, and others, in a fully secure and automated manner. LIKE.TG Activate has taken advantage of its robust analytics engine that powers a seamless flow of analysis-ready customer and product data. But, integrating this complex data from a diverse set of customers product analytics platforms is challenging; hence LIKE.TG Activate comes into the picture. LIKE.TG Activate has strong integration with other data sources that allows you to extract data make it analysis-ready. Now, become customer-centric and data-driven like never before! Give LIKE.TG Activate a try bysigning up for a 14-day free trial today.
 Connecting DynamoDB to Redshift – 2 Easy Methods
Connecting DynamoDB to Redshift – 2 Easy Methods
DynamoDB is Amazon’s document-oriented, high-performance, NoSQL Database. Given it is a NoSQL Database, it is hard to run SQL queries to analyze the data. It is essential to move data from DynamoDB to Redshift, convert it into a relational format for seamless analysis.This article will give you a comprehensive guide to set up DynamoDB to Redshift Integration. It will also provide you with a brief introduction to DynamoDB and Redshift. You will also explore 2 methods to Integrate DynamoDB and Redshift in the further sections. Let’s get started. Prerequisites You will have a much easier time understanding the ways for setting up DynamoDB to Redshift Integration if you have gone through the following aspects: An active AWS (Amazon Web Service) account.Working knowledge of Database and Data Warehouse.A clear idea regarding the type of data is to be transferred.Working knowledge of Amazon DynamoDB and Amazon Redshift would be an added advantage. Solve your data replication problems with LIKE.TG ’s reliable, no-code, automated pipelines with 150+ connectors.Get your free trial right away! Introduction to Amazon DynamoDB Fully managed by Amazon, DynamoDB is a NoSQL database service that provides high-speed and highly scalable performance. DynamoDB can handle around 20 million requests per second. Its serverless architecture and on-demand scalability make it a solution that is widely preferred. To know more about Amazon DynamoDB, visit this link. Introduction to Amazon Redshift A widely used Data Warehouse, Amazon Redshift is an enterprise-class RDBMS. Amazon Redshift provides a high-performance MPP, columnar storage set up, highly efficient targeted data compression encoding schemes, making it a natural choice for Data Warehousing and analytical needs. Amazon Redshift has excellent business intelligence abilities and a robust SQL-based interface. Amazon Redshift allows you to perform complex data analysis queries, complex joins with other tables in your AWS Redshift cluster and queries can be used in any reporting application to create dashboards or reports. To know more about Amazon Redshift, visit this link. Methods to Set up DynamoDb to Redshift Integration This article delves into both the manual and using LIKE.TG methods in depth. You will also see some of the pros and cons of these approaches and would be able to pick the best method based on your use case.Below are the two methods: Method 1: Using Copy Utility to Manually Set up DynamoDB to Redshift IntegrationMethod 2: Using LIKE.TG Data to Set up DynamoDB to Redshift Integration Method 1: Using Copy Utility to Manually Set up DynamoDB to Redshift Integration As a prerequisite, you must have a table created in Amazon Redshift before loading data from the DynamoDB table to Redshift. As we are copying data from NoSQL DB to RDBMS, we need to apply some changes/transformations before loading it to the target database. For example, some of the DynamoDB data types do not correspond directly to those of Amazon Redshift. While loading, one should ensure that each column in the Redshift table is mapped to the correct data type and size. Below is the step-by-step procedure to set up DynamoDB to Redshift Integration. Step 1: Before you migrate data from DynamoDB to Redshift create a table in Redshift using the following command as shown by the image below. Step 2: Create a table in DynamoDB by logging into the AWS console as shown below. Step 3: Add data into DynamoDB Table by clicking on Create Item. Step 4: Use the COPY command to copy data from DynamoDB to Redshift in the Employee table as shown below. copy emp.emp from 'dynamodb://Employee' iam_role 'IAM_Role' readratio 10; Step 5: Verify that data got copied successfully. Limitations of using Copy Utility to Manually Set up DynamoDB to Redshift Integration There are a handful of limitations while performing ETL from DynamoDB to Redshift using the Copy utility. Read the following: DynamoDB table names can contain up to 255 characters, including ‘.’ (dot) and ‘-‘ (dash) characters, and are case-sensitive. However, Amazon Redshift table names are limited to 127 characters, cannot include dots or dashes, and are not case-sensitive. Also, we cannot use Amazon Redshift reserved words. Unlike SQL Databases, DynamoDB does not support NULL. Interpretation of empty or blank attribute values in DynamoDB should be specified to Redshift. In Redshift, these can be treated as either NULLs or empty fields.Following data parameters are not supported alongwith COPY from DynamoDB:FILLRECORDESCAPEIGNOREBLANKLINESIGNOREHEADERNULLREMOVEQUOTESACCEPTINVCHARSMANIFESTENCRYPT However, apart from the above-mentioned limitations, the COPY command leverages Redshift’s massively parallel processing(MPP) architecture to read and stream data in parallel from an Amazon DynamoDB table. By leveraging Redshiftdistribution keys, you can make the best out of Redshift’s parallel processing architecture. Method 2: Using LIKE.TG Data to Set up DynamoDB to Redshift Integration LIKE.TG Data, a No-code Data Pipeline, helps you directly transfer data from Amazon DynamoDB and100+ other data sourcesto Data Warehouses such as Amazon Redshift, Databases, BI tools, or a destination of your choice in a completely hassle-free automated manner. LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. LIKE.TG Data takes care of all your data preprocessing needs and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. Loading data into Amazon Redshift using LIKE.TG is easier, reliable, and fast. LIKE.TG is a no-code automated data pipeline platform that solves all the challenges described above. You move data from DynamoDB to Redshift in the following two steps without writing any piece of code. Authenticate Data Source: Authenticate and connect your Amazon DynamoDB account as a Data Source. To get more details about Authenticating Amazon DynamoDB with LIKE.TG Data visit here. Configure your Destination: Configure your Amazon Redshift account as the destination. To get more details about Configuring Redshift with LIKE.TG Data visit thislink. You now have a real-time pipeline for syncing data from DynamoDB to Redshift. Sign up here for a 14-Day Free Trial! Here are more reasons to try LIKE.TG : Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Schema Management: LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to the destination schema.Minimal Learning: LIKE.TG , with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.LIKE.TG Is Built To Scale: As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency.Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.Live Monitoring: LIKE.TG allows you to monitor the data flow and check where your data is at a particular point in time. Methods to Set up DynamoDB to Redshift Integration Method 1: Using Copy Utility to Manually Set up DynamoDB to Redshift Integration This method involves the use of COPY utility to set up DynamoDB to Redshift Integration. This process of writing custom code to perform DynamoDB to Redshift replication is tedious and needs a whole bunch of precious engineering resources invested in this. As your data grows, the complexities will grow too, making it necessary to invest resources on an ongoing basis for monitoring and maintenance. Method 2: Using LIKE.TG Data to Set up DynamoDB to Redshift Integration LIKE.TG Data is an automated Data Pipeline platform that can move your data from Optimizely to MySQL very quickly without writing a single line of code. It is simple, hassle-free, and reliable. Moreover, LIKE.TG offers a fully-managed solution to set up data integration from100+ data sources(including 30+ free data sources)and will let you directly load data to a Data Warehouse such as Snowflake, Amazon Redshift, Google BigQuery, etc. or the destination of your choice. It will automate your data flow in minutes without writing any line of code. Its Fault-Tolerant architecture makes sure that your data is secure and consistent. LIKE.TG provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data. Get Started with LIKE.TG for Free Conclusion The process of writing custom code to perform DynamoDB to Redshift replication is tedious and needs a whole bunch of precious engineering resources invested in this. As your data grows, the complexities will grow too, making it necessary to invest resources on an ongoing basis for monitoring and maintenance. LIKE.TG handles all the aforementioned limitations automatically, thereby drastically reducing the effort that you and your team will have to put in. Visit our Website to Explore LIKE.TG Businesses can use automated platforms like LIKE.TG Data to set this integration and handle the ETL process. It helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you a hassle-free experience. Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs. Share your experience of setting up DynamoDB to Redshift Integration in the comments section below!
 Google Ads to Redshift Simplified: 2 Easy Methods
Google Ads to Redshift Simplified: 2 Easy Methods
Your business uses Google Ads heavily to acquire more customers and build your brand. Given the importance of this data, moving data from Google Ads to a robust Data Warehouse Redshift for advanced analytics is a step in the right direction. Google Ads is an Advertising Platform from Google that provides you the tools for launching Ad Campaigns, Product Listing, or Videos to your users. On the other hand, Amazon Redshift is a Cloud-based Data Warehousing solution from Amazon Web Services (AWS).This blog will introduce you to Google Ads and Amazon Redshift. It will also discuss 2 approaches so that you can weigh your options and choose wisely while loading data from Google Ads to Redshift. The 1st method is completely manual and demands technical proficiency while the 2nd method uses LIKE.TG Data. Introduction to Google Ads Google Ads is an Online Advertising Platform that allows businesses to showcase highly personalized ads in various formats such as Text Ads, Video Ads, Image Ads. Advertising copy is placed on pages where Google Ads things are relevant. Businesses can choose to pay Google basis a flexible model (Pay Per Click or Pay for the advertisement shown). Given the reach that Google has, this has become one of the most favorite advertising channels for modern Marketers. For more information on Google Ads, click here. Introduction to Amazon Redshift AWS Redshift is a Data Warehouse managed by Amazon Web Services (AWS). It is built using MPP (massively parallel processing) architecture and has the capacity to store large sets of data and perform advanced analytics. Designed to run complex analytical workloads in a cost-efficient fashion, Amazon Redshift has emerged to be a popular Cloud Data Warehouse choice for modern data teams. For more information on Amazon Redshift, click here. Methods to Load Data from Google Ads to Redshift Method 1: Load Data from Google Ads to Redshift by Building ETL ScriptsThis method would need a huge investment on the engineering side. A group of engineers would need to understand both Google Ads and Redshift ecosystems and hand code a custom solution to move data.Method 2: Load Data from Google Ads to Redshift using LIKE.TG DataLIKE.TG comes pre-built with integration for both Google Ads and Redshift. With a few simple clicks, a sturdy Data Replication setup can be created from Google Ads to Redshift for free. Since LIKE.TG is a managed platform, you would not need to invest in engineering resources. LIKE.TG will handle the groundwork while your analysts can work with Redshift to uncover insights. Get Started with LIKE.TG for free Methods to Load Data from Google Ads to Redshift Majorly there are 2 methods through which you can load your data from Google Ads to Redshift: Method 1: Load Data from Google Ads to Redshift by Building ETL ScriptsMethod 2: Load Data from Google Ads to Redshift using LIKE.TG Data This section will discuss the above 2 approaches in detail. In the end, you will have a deep understanding of both and you will be able to make the right decision by weighing the pros and cons of each. Now, let’s walk through these methods one by one. Method 1: Load Data from Google Ads to Redshift by Building ETL Scripts This method includes Manual Integration between Google Ads and Redshift. It demands technical knowledge and experience in working with Google Ads and Redshift. Following are the steps to integrate and load data from Google Ads to Redshift: Step 1: Extracting Data from Google AdsStep 2: Loading Google Ads Data to Redshift Step 1: Extracting Data from Google Ads Applications interact with the Google Ads platform using Google Ads API. The Google Ads API is implemented using SOAP (Simple Object Access Protocol) and doesn’t support RESTful implementation. A number of different libraries are offered that could be used with many programming languages. The following languages and frameworks are officially supported. PythonPHPJAVA.NETRubyPERL Google Ads API is quite complex and exposes many functionalities to the user. One can pull out a number of reports using Google Ads API. The granularity of the results you would need can also be specified by passing specific parameters. You can decide the data you want to get in 2 ways. By using an AWQL-based report definitionBy using XML-based report definition Most Google Ads APIs are queried using AWQL which is similar to SQL. The following output formats are supported. CSV – Comma separated values formatCSV FOR EXCEL – MS excel compatible formatTSV – Tab separated valueXML – Extensible markup language formatGZIPPED-CSV – Compressed csvGZIPPED-XML – Compressed xml You can read more about Data Extraction from Google Ads here. Once you have the necessary data extracted from Google Ads, the next step would be to load it into Redshift. Step 2: Loading Google Ads Data to Redshift As a prerequisite, you will need to create a Redshift table and map the schema from the extracted Google Ads data. When mapping the schema, you should be careful to map each attribute to the right data types supported by Redshift. Redshift supports the following data types: INTSMALLINTBIGINTDECIMALVARCHARCHARDATETIMESTAMPREALDOUBLE PRECISIONBOOLEAN Design a schema and map the data from the source. Follow the best practicespublished by Amazon when designing the Redshift database. While Redshift allows us to directly insert data into its tables, this is not the most recommended approach. Avoid using the INSERT command as it loads the data row by row. This slows the process because Redshift is not optimized to load data in this way. Instead, load the data to Amazon S3 and use the copy command to load it to Redshift. This is very useful, especially when handling large volumes of data. Limitations of Loading Data from Google Ads to Redshift Using Custom Code Accessing Google Ads Data in Real-time: After successfully creating a program that loads data from Google ads to the Redshift warehouse, you will be required to deal with the challenge of loading new and updated data. You may decide to replicate the data in real-time each time a new row or updated data is created. This process is slower and resource-intensive. Therefore, you will be required to write additional code and build cron jobs to run this in a continuous loop.Infrastructure Maintenance: Google ads may update their APIs or something may break at Redshift’s end unexpectedly. In order to save your business from irretrievable data loss, you will be required to constantly maintain the code and monitor the health of the infrastructure. Ability to Transform: The above approach only allows you to move data from Google Ads to Redshift as is. In case you are looking to clean/transform the data before loading to the warehouse – say you want to convert currencies or standardize time zones in which ads were run, this would not be possible using the previous approach. Method 2: Load Data from Google Ads to Redshift using LIKE.TG Data LIKE.TG Data, a No-code Data Pipeline helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. It supports 100+ data sources(including 40+ free sources) including Google Ads, etc., for free and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. LIKE.TG loads the data onto the desired Data Warehouse, enriches the data, and transforms it into an analysis-ready form without writing a single line of code. Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different Business Intelligence (BI) tools as well. LIKE.TG can move data from Google Ads to Redshift seamlessly in 2 simple steps: Step 1: Configuring the Source Navigate to the Asset Palette and click on Pipelines.Now, click on the +CREATE button and select Google Ads as the source for data migration.In theConfigure your Google Adspage, click+ ADD GOOGLE ADS ACCOUNT which will redirect you to the Google Ads login page.Login to your Google Ads account and click on Allow to authorize LIKE.TG to access your Google Ads data. In theConfigure your Google Ads Sourcepage, fill all the required fields Step 2: Configuring the Destination Once you have configured the source, it’s time to manage the destination. navigate to the Asset Palette and click on Destination.Click on the +CREATE button and select Amazon Redshift as the destination.In theConfigure your Amazon Redshift Destinationpage, specify all the necessary details. LIKE.TG will now take care of all the heavy-weight lifting to move data from Google Ads to Redshift. Get Started with LIKE.TG for free Advantages of Using LIKE.TG Listed below are the advantages of using LIKE.TG Data over any other Data Pipeline platform: Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Schema Management: LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to the destination schema.Minimal Learning: LIKE.TG , with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.LIKE.TG Is Built To Scale: As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency.Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.Live Monitoring: LIKE.TG allows you to monitor the data flow and check where your data is at a particular point in time. Conclusion The article introduced you to Google Ads and Amazon Redshift. It provided 2 methods that you can use for loading data from Google Ads to Redshift. The 1st method includes Manual Integration while the 2nd method uses LIKE.TG Data. With the complexity involves in Manual Integration, businesses are leaning more towards Automated and Continous Integration. This is not only hassle-free but also easy to operate and does not require any technical proficiency. In such a case, LIKE.TG Data is the right choice for you! It will help simplify the Marketing Analysis. LIKE.TG Data supports platforms like Google Ads, etc., for free. Visit our Website to Explore LIKE.TG In order to do Advanced Data Analytics effectively, you will require to have reliable and updated Google Ads data. Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand What are your thoughts on moving data from Google Ads to Redshift? Let us know in the comments.
 MongoDB to Redshift Data Transfer: 2 Easy Methods
MongoDB to Redshift Data Transfer: 2 Easy Methods
If you are looking to move data from MongoDB to Redshift, I reckon that you are trying to upgrade your analytics set up to a modern data stack. Great move!Kudos to you for taking up this mammoth of a task! In this blog, I have tried to share my two cents on how to make the data migration from MongoDB to Redshift easier for you. Before we jump to the details, I feel it is important to understand a little bit on the nuances of how MongoDB and Redshift operate. This will ensure you understand the technical nuances that might be involved in MongoDB to Redshift ETL. In case you are already an expert at this, feel free to skim through these sections or skip them entirely. What is MongoDB? MongoDB distinguishes itself as a NoSQL database program. It uses JSON-like documents along with optional schemas. MongoDB is written in C++. MongoDB allows you to address a diverse set of data sets, accelerate development, and adapt quickly to change with key functionalities like horizontal scaling and automatic failover. MondoDB is a best RDBMS when you have a huge data volume of structured and unstructured data. It’s features make scaling and flexibility smooth. These are available for data integration, load balancing, ad-hoc queries, sharding, indexing, etc. Another advantage is that MongoDB also supports all common operating systems (Linux, macOS, and Windows). It also supports C, C++, Go, Node.js, Python, and PHP. What is Amazon Redshift? Amazon Redshift is essentially a storage system that allows companies to store petabytes of data across easily accessible “Clusters” that you can query in parallel. Every Amazon Redshift Data Warehouse is fully managed which means that the administrative tasks like maintenance backups, configuration, and security are completely automated. Suppose, you are a data practitioner who wants to use Amazon Redshift to work with Big Data. It will make your work easily scalable due to its modular node design. It also us you to gain more granular insight into datasets, owing to the ability of Amazon Redshift Clusters to be further divided into slices. Amazon Redshift’s multi-layered architecture allows multiple queries to be processed simultaneously thus cutting down on waiting times. Apart from these, there are a few more benefits of Amazon Redshift you can unlock with the best practices in place. Main Features of Amazon Redshift When you submit a query, Redshift cross checks the result cache for a valid and cached copy of the query result. When it finds a match in the result cache, the query is not executed. On the other hand, it uses a cached result to reduce runtime of the query. You can use the Massive Parallel Processing (MPP) feature for writing the most complicated queries when dealing with large volume of data. Your data is stored in columnar format in Redshift tables. Therefore, the number of disk I/O requests to optimize analytical query performance is reduced. Why perform MongoDB to Redshift ETL? It is necessary to bring MongoDB’s data to a relational format data warehouse like AWS Redshift to perform analytical queries. It is simple and cost-effective to efficiently analyze all your data by using a real-time data pipeline. MongoDB is document-oriented and uses JSON-like documents to store data. MongoDB doesn’t enforce schema restrictions while storing data, the application developers can quickly change the schema, add new fields and forget about older ones that are not used anymore without worrying about tedious schema migrations. Owing to the schema-less nature of a MongoDB collection, converting data into a relational format is a non-trivial problem for you. In my experience in helping customers set up their modern data stack, I have seen MongoDB be a particularly tricky database to run analytics on. Hence, I have also suggested an easier / alternative approach that can help make your journey simpler. In this blog, I will talk about the two different methods you can use to set up a connection from MongoDB to Redshift in a seamless fashion: Using Custom ETL Scripts and with the help of a third-party tool, LIKE.TG . What Are the Methods to Move Data from MongoDB to Redshift? These are the methods we can use to move data from MongoDB to Redshift in a seamless fashion: Method 1: Using Custom Scripts to Move Data from MongoDB to Redshift Method 2: Using an Automated Data Pipeline Platform to Move Data from MongoDB to Redshift Integrate MongoDB to RedshiftGet a DemoTry it Method 1: Using Custom Scripts to Move Data from MongoDB to Redshift Following are the steps we can use to move data from MongoDB to Redshift using Custom Script: Step 1: Use mongoexport to export data. mongoexport --collection=collection_name --db=db_name --out=outputfile.csv Step 2: Upload the .json file to the S3 bucket.2.1: Since MongoDB allows for varied schema, it might be challenging to comprehend a collection and produce an Amazon Redshift table that works with it. For this reason, before uploading the file to the S3 bucket, you need to create a table structure.2.2: Installing the AWS CLI will also allow you to upload files from your local computer to S3. File uploading to the S3 bucket is simple with the help of the AWS CLI. To upload.csv files to the S3 bucket, use the command below if you have previously installed the AWS CLI. You may use the command prompt to generate a table schema after transferring.csv files into the S3 bucket. AWS S3 CP D:\outputfile.csv S3://S3bucket01/outputfile.csv Step 3: Create a Table schema before loading the data into Redshift. Step 4: Using the COPY command load the data from S3 to Redshift.Use the following COPY command to transfer files from the S3 bucket to Redshift if you’re following Step 2 (2.1). COPY table_name from 's3://S3bucket_name/table_name-csv.tbl' 'aws_iam_role=arn:aws:iam::<aws-account-id>:role/<role-name>' csv; Use the COPY command to transfer files from the S3 bucket to Redshift if you’re following Step 2 (2.2). Add csv to the end of your COPY command in order to load files in CSV format. COPY db_name.table_name FROM ‘S3://S3bucket_name/outputfile.csv’ 'aws_iam_role=arn:aws:iam::<aws-account-id>:role/<role-name>' csv; We have successfully completed MongoDB Redshift integration. For the scope of this article, we have highlighted the challenges faced while migrating data from MongoDB to Amazon Redshift. Towards the end of the article, a detailed list of advantages of using approach 2 is also given. You can check out Method 1 on our other blog and know the detailed steps to migrate MongoDB to Amazon Redshift. Limitations of using Custom Scripts to Move Data from MongoDB to Redshift Here is a list of limitations of using the manual method of moving data from MongoDB to Redshift: Schema Detection Cannot be Done Upfront: Unlike a relational database, a MongoDB collection doesn’t have a predefined schema. Hence, it is impossible to look at a collection and create a compatible table in Redshift upfront. Different Documents in a Single Collection: Different documents in single collection can have a different set of fields. A document in a collection in MongoDB can have a different set of fields. { "name": "John Doe", "age": 32, "gender": "Male" } { "first_name": "John", "last_name": "Doe", "age": 32, "gender": "Male" } Different documents in a single collection can have incompatible field data types. Hence, the schema of the collection cannot be determined by reading one or a few documents. 2 documents in a single MongoDB collection can have fields with values of different types. { "name": "John Doe", "age": 32, "gender": "Male" "mobile": "(424) 226-6998" } { "name": "John Doe", "age": 32, "gender": "Male", "mobile": 4242266998 } The fieldmobile is a string and a number in the above documents respectively. It is a completely valid state in MongoDB. In Redshift, however, both these values either will have to be converted to a string or a number before being persisted. New Fields can be added to a Document at Any Point in Time: It is possible to add columns to a document in MongoDB by running a simple update to the document. In Redshift, however, the process is harder as you have to construct and run ALTER statements each time a new field is detected. Character Lengths of String Columns: MongoDB doesn’t put a limit on the length of the string columns. It has a 16MB limit on the size of the entire document. However, in Redshift, it is a common practice to restrict string columns to a certain maximum length for better space utilization. Hence, each time you encounter a longer value than expected, you will have to resize the column. Nested Objects and Arrays in a Document: A document can have nested objects and arrays with a dynamic structure. The most complex of MongoDB ETL problems is handling nested objects and arrays. { "name": "John Doe", "age": 32, "gender": "Male", "address": { "street": "1390 Market St", "city": "San Francisco", "state": "CA" }, "groups": ["Sports", "Technology"] } MongoDB allows nesting objects and arrays to several levels. In a complex real-life scenario is may become a nightmare trying to flatten such documents into rows for a Redshift table. Data Type Incompatibility between MongoDB and Redshift: Not all data types of MongoDB are compatible with Redshift. ObjectId, Regular Expression, Javascript are not supported by Redshift. While building an ETL solution to migrate data from MongoDB to Redshift from scratch, you will have to write custom code to handle these data types. Method 2: Using Third Pary ETL Tools to Move Data from MongoDB to Redshift White using the manual approach works well, but using an automated data pipeline tool like LIKE.TG can save you time, resources and costs. LIKE.TG Data is a No-code Data Pipeline platform that can help load data from any data source, such as databases, SaaS applications, cloud storage, SDKs, and streaming services to a destination of your choice. Here’s how LIKE.TG overcomes the challenges faced in the manual approach for MongoDB to Redshift ETL: Dynamic expansion for Varchar Columns: LIKE.TG expands the existing varchar columns in Redshift dynamically as and when it encounters longer string values. This ensures that your Redshift space is used wisely without you breaking a sweat. Splitting Nested Documents with Transformations: LIKE.TG lets you split the nested MongoDB documents into multiple rows in Redshift by writing simple Python transformations. This makes MongoDB file flattening a cakewalk for users. Automatic Conversion to Redshift Data Types: LIKE.TG converts all MongoDB data types to the closest compatible data type in Redshift. This eliminates the need to write custom scripts to maintain each data type, in turn, making the migration of data from MongoDB to Redshift seamless. Here are the steps involved in the process for you: Step 1: Configure Your Source Load Data from LIKE.TG to MongoDB by entering details like Database Port, Database Host, Database User, Database Password, Pipeline Name, Connection URI, and the connection settings. Step 2: Intgerate Data Load data from MongoDB to Redshift by providing your Redshift databases credentials like Database Port, Username, Password, Name, Schema, and Cluster Identifier along with the Destination Name. LIKE.TG supports 150+ data sources including MongoDB and destinations like Redshift, Snowflake, BigQuery and much more. LIKE.TG ’s fault-tolerant and scalable architecture ensures that the data is handled in a secure, consistent manner with zero data loss. Give LIKE.TG a try and you can seamlessly export MongoDB to Redshift in minutes. GET STARTED WITH LIKE.TG FOR FREE For detailed information on how you can use the LIKE.TG connectors for MongoDB to Redshift ETL, check out: MongoDB Source Connector Redshift Destination Connector Additional Resources for MongoDB Integrations and Migrations Stream data from mongoDB Atlas to BigQuery Move Data from MongoDB to MySQL Connect MongoDB to Snowflake Connect MongoDB to Tableau Conclusion In this blog, I have talked about the 2 different methods you can use to set up a connection from MongoDB to Redshift in a seamless fashion: Using Custom ETL Scripts and with the help of a third-party tool, LIKE.TG . Outside of the benefits offered by LIKE.TG , you can use LIKE.TG to migrate data from an array of different sources – databases, cloud applications, SDKs, and more. This will provide the flexibility to instantly replicate data from any source like MongoDB to Redshift. More related reads: Creating a table in Redshift Redshift functions You can additionally model your data, build complex aggregates and joins to create materialized views for faster query executions on Redshift. You can define the interdependencies between various models through a drag and drop interface with LIKE.TG ’s Workflows to convert MongoDB data to Redshift.
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					10 Benefits That Explain the Importance of CRM in Banking
10 Benefits That Explain the Importance of CRM in Banking
The banking industry is undergoing a digital transformation, and customer relationship management (CRM) systems are at the forefront of this change. By providing a centralised platform for customer data, interactions, and analytics, CRMs empower banks to deliver personalised and efficient services, fostering customer loyalty and driving business growth. We’ll look closer at the significance of CRM in banking, exploring its numerous benefits, addressing challenges in adoption, and highlighting future trends and innovations. Additionally, we present a compelling case study showcasing a successful CRM implementation in the banking sector. 10 Questions to Ask When Choosing a CRM in Banking When selecting a top CRM platform for your banking institution, it is necessary to carefully evaluate potential solutions to ensure they align with your specific requirements and objectives. Here are 10 key questions to ask during the selection process: 1. Does the CRM integrate with your existing, financial and banking organisation and systems? A seamless integration between your CRM and existing banking systems is essential to avoid data silos and ensure a holistic view of customer interactions. Look for a CRM that can easily integrate with your core banking system, payment platforms, and other relevant applications. 2. Can the CRM provide a 360-degree view of your customers? A CRM should offer a unified platform that consolidates customer data from various touchpoints, including online banking, mobile banking, branches, and contact centres. This enables bank representatives to access a complete customer profile, including account information, transaction history, and past interactions, resulting in more personalised and efficient customer service. 3. Does the CRM offer robust reporting and analytics capabilities? Leverage the power of data by selecting a CRM that provides robust reporting and analytics capabilities. This will allow you to analyse customer behaviour, identify trends, and gain actionable insights into customer needs and preferences. Look for a CRM that offers customisable reports, dashboards, and data visualisation tools to empower your bank with data-driven decision-making. 4. Is the CRM user-friendly and easy to implement? A user-friendly interface is essential for ensuring that your bank’s employees can effectively utilise the CRM. Consider the technical expertise of your team and opt for a CRM with an intuitive design, clear navigation, and minimal training requirements. Additionally, evaluate the implementation process to ensure it can be completed within your desired timeframe and budget. What is a CRM in the Banking Industry? Customer relationship management (CRM) is a crucial technology for banks to optimise customer service, improve operational efficiency, and drive business growth. A CRM system acts as a centralised platform that empowers banks to manage customer interactions, track customer information, and analyse customer data. By leveraging CRM capabilities, banks can also gain deeper insights and a larger understanding of their customers’ needs, preferences, and behaviours, enabling them to deliver personalised and exceptional banking experiences. CRM in banking fosters stronger customer relationships by facilitating personalised interactions. With a CRM system, banks can capture and store customer data, including personal information, transaction history, and communication preferences. This data enables bank representatives to have informed conversations with customers, addressing their specific needs and providing tailored financial solutions. Personalised interactions enhance customer satisfaction, loyalty, and overall banking experience. CRM enhances operational efficiency and productivity within banks. By automating routine tasks such as data entry, customer service ticketing, and report generation, banking CRM software streamlines workflows and reduces manual labour. This automation allows bank employees to focus on higher-value activities, such as customer engagement and financial advisory services. Furthermore, CRM provides real-time access to customer information, enabling employees to quickly retrieve and update customer data, thereby enhancing operational efficiency. Additionally, CRM empowers banks to analyse customer data and derive valuable insights. With robust reporting and analytics capabilities, banks can identify customer segments, analyse customer behaviour, and measure campaign effectiveness. This data-driven approach enables banks to make informed decisions, optimise marketing strategies, and develop targeted products and services that cater to specific customer needs. CRM also plays a vital role in risk management and compliance within the banking industry. By integrating customer data with regulatory requirements, banks can effectively monitor transactions, detect suspicious activities, and mitigate fraud risks. This ensures compliance with industry regulations and safeguards customer information. In summary, CRM is a transformative technology that revolutionises banking operations. By fostering personalised customer experiences and interactions, enhancing operational efficiency, enabling data-driven decision-making, and ensuring risk management, CRM empowers banks to deliver superior customer service, drive business growth, and maintain a competitive edge. The 10 Business Benefits of Using a Banking CRM 1. Streamlined Customer Interactions: CRMs enable banks to centralise customer data, providing a holistic view of each customer’s interactions with the bank. This allows for streamlined and personalised customer service, improving customer satisfaction and reducing the time and effort required to resolve customer queries. 2. Enhanced Data Management and Analytics: CRMs provide powerful data management capabilities, enabling banks to collect, store, and analyse customer data from various sources. This data can be leveraged to gain valuable insights into customer behaviour, preferences, and buying patterns. Banks can then use these insights to optimise their products, services, and marketing strategies. 3. Increased Sales and Cross-Selling Opportunities: CRMs help banks identify cross-selling and upselling opportunities by analysing customer data and identifying customer needs and preferences. By leveraging this information, banks can proactively recommend relevant products and services, increasing sales and revenue. 4. Improved Customer Retention and Loyalty: CRMs help banks build stronger customer relationships by enabling personalised interactions and providing excellent customer service. By understanding customer needs and preferences, banks can proactively address issues and provide tailored solutions, fostering customer loyalty and reducing churn. 5. Enhanced Regulatory Compliance and Risk Management: CRMs assist banks in complying with industry regulations and managing risks effectively. By centralising customer data and tracking customer interactions, banks can easily generate reports and demonstrate compliance with regulatory requirements. CRMs and other banking software programs also help in identifying and managing potential risks associated with customer transactions. 6. Improved Operational Efficiency: CRMs streamline various banking processes, including customer onboarding, loan processing, and account management. By automating repetitive tasks and providing real-time access to customer information, CRMs help banks improve operational efficiency and reduce costs. 7. Increased Employee Productivity: CRMs provide banking employees with easy access to customer data and real-time updates, enabling them to handle customer inquiries more efficiently. This reduces the time spent on administrative tasks and allows employees to focus on providing exceptional customer service. 8. Improved Decision-Making: CRMs provide banks with data-driven insights into customer behaviour and market trends. This information supports informed decision-making, enabling banks to develop and implement effective strategies for customer acquisition, retention, and growth. 9. Enhanced Customer Experience: CRMs help banks deliver a superior customer experience by providing personalised interactions, proactive problem resolution, and quick response to customer inquiries. This results in increased customer satisfaction and positive brand perception.10. Increased Profitability: By leveraging the benefits of CRM systems, banks can optimise their operations, increase sales, and reduce costs, ultimately leading to increased profitability and long-term success for financial service customers. Case studies highlighting successful CRM implementations in banking Several financial institutions have successfully implemented CRM systems to enhance their operations and customer service. Here are a few notable case studies: DBS Bank: DBS Bank, a leading financial institution in Southeast Asia, implemented a CRM system to improve customer service and cross-selling opportunities. The system provided a 360-degree view of customers, enabling the bank to tailor products and services to individual needs. As a result, DBS Bank increased customer retention by 15% and cross-selling opportunities by 20%. HDFC Bank: India’s largest private sector bank, HDFC Bank, implemented a CRM system to improve customer service and operational efficiency. The system integrated various customer touch points, such as branches, ATMs, and online banking, providing a seamless experience for customers. HDFC Bank achieved a 20% reduction in operating costs and a 15% increase in customer satisfaction. JPMorgan Chase: JPMorgan Chase, one of the largest banks in the United States, implemented a CRM system to improve customer interactions and data management. The system provided a centralised platform to track customer interactions and data, allowing the bank to gain insights into customer behaviour and preferences. As a result, JPMorgan Chase increased customer interactions by 15% and improved data accuracy by 20%. Bank of America: Bank of America, the second-largest bank in the United States, implemented a CRM system to improve sales and cross-selling opportunities. The system provided sales teams with real-time customer data, across sales and marketing efforts enabling them to tailor their pitches and identify potential cross-selling opportunities. Bank of America achieved a 10% increase in sales and a 15% increase in cross-selling opportunities.These case studies demonstrate the tangible benefits of CRM in the banking industry. By implementing CRM systems, banks can improve customer retention, customer service, cross-selling opportunities, operating costs, and marketing campaigns. Overcoming challenges to CRM adoption in banking While CRM systems offer numerous benefits to banks, their adoption can be hindered by certain challenges. One of the primary obstacles is resistance from employees who may be reluctant to embrace new technology or fear job displacement. Overcoming this resistance requires effective change management strategies, such as involving employees in the selection and implementation process, providing all-encompassing training, and addressing their concerns. Another challenge is the lack of proper training and support for employees using the CRM system. Insufficient training can lead to low user adoption and suboptimal utilisation of the system’s features. To address this, banks should invest in robust training programs that equip employees with the knowledge and skills necessary to effectively use the CRM system. Training should cover not only the technical aspects of the system but also its benefits and how it aligns with the bank’s overall goals. Integration challenges can also hinder the successful adoption of CRM software in banking. Banks often have complex IT systems and integrating a new CRM system can be a complex and time-consuming process. To overcome these challenges, banks should carefully plan the integration process, ensuring compatibility between the CRM system and existing systems. This may involve working with the CRM vendor to ensure a smooth integration process and providing adequate technical support to address any issues that arise. Data security is a critical concern for banks, and the adoption of a CRM system must address potential security risks. Banks must ensure that the CRM system meets industry standards and regulations for data protection. This includes implementing robust security measures, such as encryption, access controls, and regular security audits, to safeguard sensitive customer information. Finally, the cost of implementing and maintaining a CRM system can be a challenge for banks. CRM systems require significant upfront investment in software, hardware, and training. Banks should carefully evaluate the costs and benefits of CRM adoption, ensuring that the potential returns justify the investment. Additionally, banks should consider the ongoing costs associated with maintaining and updating the CRM system, as well as the cost of providing ongoing training and support to users. Future trends and innovations in banking CRM Navigating Evolving Banking Trends and Innovations in CRM The banking industry stands at the precipice of transformative changes, driven by a surge of innovative technologies and evolving customer expectations. Open banking, artificial intelligence (AI), blockchain technology, the Internet of Things (IoT), and voice-activated interfaces are shaping the future of banking CRM. Open banking is revolutionising the financial sphere by enabling banks to securely share customer data with third-party providers, with the customer’s explicit consent. This fosters a broader financial ecosystem, offering customers access to a varied range of products and services, while fostering healthy competition and innovation within the banking sector. AI has become an indispensable tool for banking institutions, empowering them to deliver exceptional customer experiences. AI-driven chatbots and virtual assistants provide round-the-clock support, assisting customers with queries, processing transactions, and ensuring swift problem resolution. Additionally, AI plays a pivotal role in fraud detection and risk management, safeguarding customers’ financial well-being. Blockchain technology, with its decentralised and immutable nature, offers a secure platform for financial transactions. By maintaining an incorruptible ledger of records, blockchain ensures the integrity and transparency of financial data, building trust among customers and enhancing the overall banking experience. The Internet of Things (IoT) is transforming banking by connecting physical devices to the internet, enabling real-time data collection and exchange. IoT devices monitor customer behaviour, track equipment status, and manage inventory, empowering banks to optimise operations, reduce costs, and deliver personalised services. Voice-activated interfaces and chatbots are revolutionising customer interactions, providing convenient and intuitive access to banking services. Customers can utilise voice commands or text-based chat to manage accounts, make payments, and seek assistance, enhancing their overall banking experience. These transformative trends necessitate banks’ ability to adapt and innovate continuously. By embracing these technologies and aligning them with customer needs, banks can unlock new opportunities for growth, strengthen customer relationships, and remain at the forefront of the industry. How LIKE.TG Can Help LIKE.TG is a leading provider of CRM solutions that can help banks achieve the benefits of CRM. With LIKE.TG, banks can gain a complete view of their customers, track interactions, deliver personalised experiences, and more. LIKE.TG offers a comprehensive suite of CRM tools that can be customised to meet the specific needs of banks. These tools include customer relationship management (CRM), sales and marketing automation, customer service, and analytics. By leveraging LIKE.TG, banks can improve customer satisfaction, increase revenue, and reduce costs. For example, one bank that implemented LIKE.TG saw a 20% increase in customer satisfaction, a 15% increase in revenue, and a 10% decrease in costs. Here are some specific examples of how LIKE.TG can help banks: Gain a complete view of customers: LIKE.TG provides a single, unified platform that allows banks to track all customer interactions, from initial contact to ongoing support. This information can be used to create a complete picture of each customer, which can help banks deliver more personalised and relevant experiences. Track interactions: LIKE.TG allows banks to track all interactions with customers, including phone calls, emails, chat conversations, and social media posts. This information can be used to identify trends and patterns, which can help banks improve their customer service and sales efforts. Deliver personalised experiences: LIKE.TG allows banks to create personalised experiences for each customer. This can be done by using customer data to tailor marketing campaigns, product recommendations, and customer service interactions. Increase revenue: LIKE.TG can help banks increase revenue by providing tools to track sales opportunities, manage leads, and forecast revenue. This information can be used to make informed decisions about which products and services to offer, and how to best target customers. Reduce costs: LIKE.TG can help banks reduce costs by automating tasks, streamlining processes, and improving efficiency. This can free up resources that can be used to focus on other areas of the business. Overall, LIKE.TG is a powerful CRM solution that can help banks improve customer satisfaction, increase revenue, and reduce costs. By leveraging LIKE.TG, banks can gain a competitive advantage in the rapidly changing financial services industry.

					10 Ecommerce Trends That Will Influence Online Shopping in 2024
10 Ecommerce Trends That Will Influence Online Shopping in 2024
Some ecommerce trends and technologies pass in hype cycles, but others are so powerful they change the entire course of the market. After all the innovations and emerging technologies that cropped up in 2023, business leaders are assessing how to move forward and which new trends to implement.Here are some of the biggest trends that will affect your business over the coming year. What you’ll learn: Artificial intelligence is boosting efficiency Businesses are prioritising data management and harmonisation Conversational commerce is getting more human Headless commerce is helping businesses keep up Brands are going big with resale Social commerce is evolving Vibrant video content is boosting sales Loyalty programs are getting more personalised User-generated content is influencing ecommerce sales Subscriptions are adding value across a range of industries Ecommerce trends FAQ 1. Artificial intelligence is boosting efficiency There’s no doubt about it: Artificial intelligence (AI) is changing the ecommerce game. Commerce teams have been using the technology for years to automate and personalise product recommendations, chatbot activity, and more. But now, generative and predictive AI trained on large language models (LLM) offer even more opportunities to increase efficiency and scale personalisation. AI is more than an ecommerce trend — it can make your teams more productive and your customers more satisfied. Do you have a large product catalog that needs to be updated frequently? AI can write and categorise individual descriptions, cutting down hours of work to mere minutes. Do you need to optimise product detail pages? AI can help with SEO by automatically generating meta titles and meta descriptions for every product. Need to build a landing page for a new promotion? Generative page designers let users of all skill levels create and design web pages in seconds with simple, conversational building tools. All this innovation will make it easier to keep up with other trends, meet customers’ high expectations, and stay flexible — no matter what comes next. 2. Businesses are prioritising data management and harmonisation Data is your most valuable business asset. It’s how you understand your customers, make informed decisions, and gauge success. So it’s critical to make sure your data is in order. The challenge? Businesses collect a lot of it, but they don’t always know how to manage it. That’s where data management and harmonisation come in. They bring together data from multiple sources — think your customer relationship management (CRM) and order management systems — to provide a holistic view of all your business activities. With harmonised data, you can uncover insights and act on them much faster to increase customer satisfaction and revenue. Harmonised data also makes it possible to implement AI (including generative AI), automation, and machine learning to help you market, serve, and sell more efficiently. That’s why data management and harmonisation are top priorities among business leaders: 68% predict an increase in data management investments. 32% say a lack of a complete view and understanding of their data is a hurdle. 45% plan to prioritise gaining a more holistic view of their customers. For businesses looking to take advantage of all the new AI capabilities in ecommerce, data management should be priority number one. 3. Conversational commerce is getting more human Remember when chatbot experiences felt robotic and awkward? Those days are over. Thanks to generative AI and LLMs, conversational commerce is getting a glow-up. Interacting with chatbots for service inquiries, product questions, and more via messaging apps and websites feels much more human and personalised. Chatbots can now elevate online shopping with conversational AI and first-party data, mirroring the best in-store interactions across all digital channels. Natural language, image-based, and data-driven interactions can simplify product searches, provide personalised responses, and streamline purchases for a smooth experience across all your digital channels. As technology advances, this trend will gain more traction. Intelligent AI chatbots offer customers better self-service experiences and make shopping more enjoyable. This is critical since 68% of customers say they wouldn’t use a company’s chatbot again if they had a bad experience. 4. Headless commerce is helping businesses keep up Headless commerce continues to gain steam. With this modular architecture, ecommerce teams can deliver new experiences faster because they don’t have to wait in the developer queue to change back-end systems. Instead, employees can update online interfaces using APIs, experience managers, and user-friendly tools. According to business leaders and commerce teams already using headless: 76% say it offers more flexibility and customisation. 72% say it increases agility and lets teams make storefront changes faster. 66% say it improves integration between systems. Customers reap the benefits of headless commerce, too. Shoppers get fresh experiences more frequently across all devices and touchpoints. Even better? Headless results in richer personalisation, better omni-channel experiences, and peak performance for ecommerce websites. 5. Brands are going big with resale Over the past few years, consumers have shifted their mindset about resale items. Secondhand purchases that were once viewed as stigma are now seen as status. In fact, more than half of consumers (52%) have purchased an item secondhand in the last year, and the resale market is expected to reach $70 billion by 2027. Simply put: Resale presents a huge opportunity for your business. As the circular economy grows in popularity, brands everywhere are opening their own resale stores and encouraging consumers to turn in used items, from old jeans to designer handbags to kitchen appliances. To claim your piece of the pie, be strategic as you enter the market. This means implementing robust inventory and order management systems with real-time visibility and reverse logistics capabilities. 6. Social commerce is evolving There are almost 5 billion monthly active users on platforms like Instagram, Facebook, Snapchat, and TikTok. More than two-thirds (67%) of global shoppers have made a purchase through social media this year. Social commerce instantly connects you with a vast global audience and opens up new opportunities to boost product discovery, reach new markets, and build meaningful connections with your customers. But it’s not enough to just be present on social channels. You need to be an active participant and create engaging, authentic experiences for shoppers. Thanks to new social commerce tools — like generative AI for content creation and integrations with social platforms — the shopping experience is getting better, faster, and more engaging. This trend is blurring the lines between shopping and entertainment, and customer expectations are rising as a result. 7. Vibrant video content is boosting sales Now that shoppers have become accustomed to the vibrant, attention-grabbing video content on social platforms, they expect the same from your brand’s ecommerce site. Video can offer customers a deeper understanding of your products, such as how they’re used, and what they look like from different angles. And video content isn’t just useful for ads or for increasing product discovery. Brands are having major success using video at every stage of the customer journey: in pre-purchase consultations, on product detail pages, and in post-purchase emails. A large majority (89%) of consumers say watching a video has convinced them to buy a product or service. 8. Loyalty programs are getting more personalised It’s important to attract new customers, but it’s also critical to retain your existing ones. That means you need to find ways to increase loyalty and build brand love. More and more, customers are seeking out brand loyalty programs — but they want meaningful rewards and experiences. So, what’s the key to a successful loyalty program? In a word: personalisation. Customers don’t want to exchange their data for a clunky, impersonal experience where they have to jump through hoops to redeem points. They want straightforward, exclusive offers. Curated experiences. Relevant rewards. Six out of 10 consumers want discounts in return for joining a loyalty program, and about one-third of consumers say they find exclusive or early access to products valuable. The brands that win customer loyalty will be those that use data-driven insights to create a program that keeps customers continually engaged and satisfied. 9. User-generated content is influencing ecommerce sales User-generated content (UGC) adds credibility, authenticity‌, and social proof to a brand’s marketing efforts — and can significantly boost sales and brand loyalty. In fact, one study found that shoppers who interact with UGC experience a 102.4% increase in conversions. Most shoppers expect to see feedback and reviews before making a purchase, and UGC provides value by showcasing the experiences and opinions of real customers. UGC also breaks away from generic item descriptions and professional product photography. It can show how to style a piece of clothing, for example, or how an item will fit across a range of body types. User-generated videos go a step further, highlighting the functions and features of more complex products, like consumer electronics or even automobiles. UGC is also a cost-effective way to generate content for social commerce without relying on agencies or large teams. By sourcing posts from hashtags, tagging, or concentrated campaigns, brands can share real-time, authentic, and organic social posts to a wider audience. UGC can be used on product pages and in ads, as well. And you can incorporate it into product development processes to gather valuable input from customers at scale. 10. Subscriptions are adding value across a range of industries From streaming platforms to food, clothing, and pet supplies, subscriptions have become a popular business model across industries. In 2023, subscriptions generated over $38 billion in revenue, doubling over the past four years. That’s because subscriptions are a win-win for shoppers and businesses: They offer freedom of choice for customers while creating a continuous revenue stream for sellers. Consider consumer goods brand KIND Snacks. KIND implemented a subscription service to supplement its B2B sales, giving customers a direct line to exclusive offers and flavours. This created a consistent revenue stream for KIND and helped it build a new level of brand loyalty with its customers. The subscription also lets KIND collect first-party data, so it can test new products and spot new trends. Ecommerce trends FAQ How do I know if an ecommerce trend is right for my business? If you’re trying to decide whether to adopt a new trend, the first step is to conduct a cost/benefit analysis. As you do, remember to prioritise customer experience and satisfaction. Look at customer data to evaluate the potential impact of the trend on your business. How costly will it be to implement the trend, and what will the payoff be one, two, and five years into the future? Analyse the numbers to assess whether the trend aligns with your customers’ preferences and behaviours. You can also take a cue from your competitors and their adoption of specific trends. While you shouldn’t mimic everything they do, being aware of their experiences can provide valuable insights and help gauge the viability of a trend for your business. Ultimately, customer-centric decision-making should guide your evaluation. Is ecommerce still on the rise? In a word: yes. In fact, ecommerce is a top priority for businesses across industries, from healthcare to manufacturing. Customers expect increasingly sophisticated digital shopping experiences, and digital channels continue to be a preferred purchasing method. Ecommerce sales are expected to reach $8.1 trillion by 2026. As digital channels and new technologies evolve, so will customer behaviours and expectations. Where should I start if I want to implement AI? Generative AI is revolutionising ecommerce by enhancing customer experiences and increasing productivity, conversions, and customer loyalty. But to reap the benefits, it’s critical to keep a few things in mind. First is customer trust. A majority of customers (68%) say advances in AI make it more important for companies to be trustworthy. This means businesses implementing AI should focus on transparency. Tell customers how you will use their data to improve shopping experiences. Develop ethical standards around your use of AI, and discuss them openly. You’ll need to answer tough questions like: How do you ensure sensitive data is anonymised? How will you monitor accuracy and audit for bias, toxicity, or hallucinations? These should all be considerations as you choose AI partners and develop your code of conduct and governance principles. At a time when only 13% of customers fully trust companies to use AI ethically, this should be top of mind for businesses delving into the fast-evolving technology. How can commerce teams measure success after adopting a new trend? Before implementing a new experience or ecommerce trend, set key performance indicators (KPIs) and decide how you’ll track relevant ecommerce metrics. This helps you make informed decisions and monitor the various moving parts of your business. From understanding inventory needs to gaining insights into customer behaviour to increasing loyalty, you’ll be in a better position to plan for future growth. The choice of metrics will depend on the needs of your business, but it’s crucial to establish a strategy that outlines metrics, sets KPIs, and measures them regularly. Your business will be more agile and better able to adapt to new ecommerce trends and understand customer buying patterns. Ecommerce metrics and KPIs are valuable tools for building a successful future and will set the tone for future ecommerce growth.

					10 Effective Sales Coaching Tips That Work
10 Effective Sales Coaching Tips That Work
A good sales coach unlocks serious revenue potential. Effective coaching can increase sales performance by 8%, according to a study by research firm Gartner.Many sales managers find coaching difficult to master, however — especially in environments where reps are remote and managers are asked to do more with less time and fewer resources.Understanding the sales coaching process is crucial in maximising sales rep performance, empowering reps, and positively impacting the sales organisation through structured, data-driven strategies.If you’re not getting the support you need to effectively coach your sales team, don’t despair. These 10 sales coaching tips are easy to implement with many of the tools already at your disposal, and are effective for both in-person and remote teams.1. Focus on rep wellbeingOne in three salespeople say mental health in sales has declined over the last two years, according to a recent LIKE.TG survey. One of the biggest reasons is the shift to remote work environments, which pushed sales reps to change routines while still hitting quotas. Add in the isolation inherent in virtual selling and you have a formula for serious mental and emotional strain.You can alleviate this in a couple of ways. First, create boundaries for your team. Set clear work hours and urge reps not to schedule sales or internal calls outside of these hours. Also, be clear about when reps should be checking internal messages and when they can sign off.Lori Richardson, founder of sales training company Score More Sales, advises managers to address this head-on by asking reps about their wellbeing during weekly one-on-ones. “I like to ask open-ended questions about the past week,” she said. “Questions like, ‘How did it go?’ and ‘What was it like?’ are good first steps. Then, you need to listen.”When the rep is done sharing their reflection, Richardson suggests restating the main points to ensure you’re on the same page. If necessary, ask for clarity so you fully understand what’s affecting their state of mind. Also, she urges: Don’t judge. The level of comfort required for sharing in these scenarios can only exist if you don’t jump to judgement.2. Build trust with authentic storiesFor sales coaching to work, sales managers must earn reps’ trust. This allows the individual to be open about performance challenges. The best way to start is by sharing personal and professional stories.These anecdotes should be authentic, revealing fault and weakness as much as success. There are two goals here: support reps with relatable stories so they know they’re not struggling alone, and let them know there are ways to address and overcome challenges.For example, a seasoned manager might share details about their first failed sales call as a cautionary tale – highlighting poor preparation, aggressive posturing, and lack of empathy during the conversation. This would be followed by steps the manager took to fix these mistakes, like call rehearsing and early-stage research into the prospect’s background, business, position, and pain points.3. Record and review sales callsSales coaching sessions, where recording and reviewing sales calls are key components aimed at improving sales call techniques, have become essential in today’s sales environment. Once upon a time, sales reps learned by shadowing tenured salespeople. While this is still done, it’s inefficient – and often untenable for virtual sales teams.To give sales reps the guidance and coaching they need to improve sales calls, deploy an intuitive conversation recording and analysis tool like Einstein Conversation Insights (ECI). You can analyse sales call conversations, track keywords to identify market trends, and share successful calls to help coach existing reps and accelerate onboarding for new reps. Curate both “best of” and “what not to do” examples so reps have a sense of where the guide rails are.4. Encourage self-evaluationWhen doing post-call debriefs or skill assessments – or just coaching during one-on-ones – it’s critical to have the salesperson self-evaluate. As a sales manager, you may only be with the rep one or two days a month. Given this disconnect, the goal is to encourage the sales rep to evaluate their own performance and build self-improvement goals around these observations.There are two important components to this. First, avoid jumping directly into feedback during your interactions. Relax and take a step back; let the sales rep self-evaluate.Second, be ready to prompt your reps with open-ended questions to help guide their self-evaluation. Consider questions like:What were your big wins over the last week/quarter?What were your biggest challenges and where did they come from?How did you address obstacles to sales closings?What have you learned about both your wins and losses?What happened during recent calls that didn’t go as well as you’d like? What would you do differently next time?Reps who can assess what they do well and where they can improve ultimately become more self-aware. Self-awareness is the gateway to self-confidence, which can help lead to more consistent sales.5. Let your reps set their own goalsThis falls in line with self-evaluation. Effective sales coaches don’t set focus areas for their salespeople; they let reps set this for themselves. During your one-on-ones, see if there’s an important area each rep wants to focus on and go with their suggestion (recommending adjustments as needed to ensure their goals align with those of the company). This creates a stronger desire to improve as it’s the rep who is making the commitment. Less effective managers will pick improvement goals for their reps, then wonder why they don’t get buy-in.For instance, a rep who identifies a tendency to be overly chatty in sales calls might set a goal to listen more. (Nine out of 10 salespeople say listening is more important than talking in sales today, according to a recent LIKE.TG survey.) To help, they could record their calls and review the listen-to-talk ratio. Based on industry benchmarks, they could set a clear goal metric and timeline – a 60/40 listen-to-talk ratio in four weeks, for example.Richardson does have one note of caution, however. “Reps don’t have all the answers. Each seller has strengths and gaps,” she said. “A strong manager can identify those strengths and gaps, and help reps fill in the missing pieces.”6. Focus on one improvement at a timeFor sales coaching to be effective, work with the rep to improve one area at a time instead of multiple areas simultaneously. With the former, you see acute focus and measurable progress. With the latter, you end up with frustrated, stalled-out reps pulled in too many directions.Here’s an example: Let’s say your rep is struggling with sales call openings. They let their nerves get the best of them and fumble through rehearsed intros. Over the course of a year, encourage them to practice different kinds of openings with other reps. Review their calls and offer insight. Ask them to regularly assess their comfort level with call openings during one-on-ones. Over time, you will see their focus pay off.7. Ask each rep to create an action planOpen questioning during one-on-ones creates an environment where a sales rep can surface methods to achieve their goals. To make this concrete, have the sales rep write out a plan of action that incorporates these methods. This plan should outline achievable steps to a desired goal with a clearly defined timeline. Be sure you upload it to your CRM as an attachment or use a tool like Quip to create a collaborative document editable by both the manager and the rep. Have reps create the plan after early-quarter one-on-ones and check in monthly to gauge progress (more on that in the next step).Here’s what a basic action plan might look like:Main goal: Complete 10 sales calls during the last week of the quarterSteps:Week 1: Identify 20-25 prospectsWeek 2: Make qualifying callsWeek 3: Conduct needs analysis (discovery) calls, prune list, and schedule sales calls with top prospectsWeek 4: Lead sales calls and close dealsThe power of putting pen to paper here is twofold. First, it forces the sales rep to think through their plan of action. Second, it crystallises their thinking and cements their commitment to action.8. Hold your rep accountableAs businessman Louis Gerstner, Jr. wrote in “Who Says Elephants Can’t Dance?”, “people respect what you inspect.” The effective manager understands that once the plan of action is in place, their role as coach is to hold the sales rep accountable for following through on their commitments. To support them, a manager should ask questions during one-on-ones such as:What measurable progress have you made this week/quarter?What challenges are you facing?How do you plan to overcome these challenges?You can also review rep activity in your CRM. This is especially easy if you have a platform that combines automatic activity logging, easy pipeline inspection, and task lists with reminders. If you need to follow up, don’t schedule another meeting. Instead, send your rep a quick note via email or a messaging tool like Slack to level-set.9. Offer professional development opportunitiesAccording to a study by LinkedIn, 94% of employees would stay at a company longer if it invested in their career. When companies make an effort to feed their employees’ growth, it’s a win-win. Productivity increases and employees are engaged in their work.Book clubs, seminars, internal training sessions, and courses are all great development opportunities. If tuition reimbursement or sponsorship is possible, articulate this up front so reps know about all available options.Richardson adds podcasts to the list. “Get all of your salespeople together to talk about a podcast episode that ties into sales,” she said. “Take notes, pull key takeaways and action items, and share a meeting summary the next day with the group. I love that kind of peer engagement. It’s so much better than watching a dull training video.”10. Set up time to share failures — and celebrationsAs Forbes Council member and sales vet Adam Mendler wrote of sales teams, successful reps and executives prize learning from failure. But as Richardson points out, a lot of coaches rescue their reps before they can learn from mistakes: “Instead of letting them fail, they try to save an opportunity,” she said. “But that’s not scalable and doesn’t build confidence in the rep.”Instead, give your reps the freedom to make mistakes and offer them guidance to grow through their failures. Set up a safe space where reps can share their mistakes and learnings with the larger team — then encourage each rep to toss those mistakes on a metaphorical bonfire so they can move on.By embracing failure as a learning opportunity, you also minimise the likelihood of repeating the same mistakes. Encourage your reps to document the circumstances that led to a missed opportunity or lost deal. Review calls to pinpoint where conversations go awry. Study failure, and you might be surprised by the insights that emerge.Also — and equally as important — make space for celebrating big wins. This cements best practices and offers positive reinforcement, which motivates reps to work harder to hit (or exceed) quota.Next steps for your sales coaching programA successful sales coach plays a pivotal role in enhancing sales rep performance and elevating the entire sales organisation. Successful sales coaching requires daily interaction with your team, ongoing training, and regular feedback, which optimises sales processes to improve overall sales performance. As Lindsey Boggs, global director of sales development at Quantum Metric, noted, it also requires intentional focus and a strategic approach to empower the sales team, significantly impacting the sales organisation.“Remove noise from your calendar so you can focus your day on what’s going to move the needle the most — coaching,” she said. Once that’s prioritised, follow the best practices above to help improve your sales reps’ performance, focusing on individual rep development as a key aspect of sales coaching. Remember: coaching is the key to driving sales performance.Steven Rosen, founder of sales management training company STAR Results, contributed to this article.
企业管理
AI translation apps: Benefits for your travels?
AI translation apps
Benefits for your travels?
This article explains the benefits of AI translation apps for travelers, which offer a practical and efficient solution worldwide.Despite the increasing accessibility of international travel, language barriers continue to pose a significant challenge. At LIKE.TG, our goal is to help you explore the world more easilyThe Revolution of AI in TranslationAI technology has revolutionized language translation, providing unprecedented accuracy and contextualization.These applications continuously learn, improving their ability to understand and translate linguistic and cultural nuances with each update.Benefits of AI Translation AppsTravel without language barriersImagine asking for directions, interacting with locals, or even resolving emergencies in a language you don’t speak.AI translation apps make it all possible, removing one of the biggest obstacles for travelers: language.Instant communicationImagine looking at a menu in an Italian restaurant and every dish sounds like a Harry Potter spell. This is where your AI translation app acts as your personal wand.Imagine having a magic button that allows you to instantly understand and speak any language. Well, in the real world, that “wand” fits in your pocket and is called an AI translation app.These apps are like having a personal mini translator with you 24/7, ready to help you order that strange dish on the menu without ending up eating something you can’t even pronounce.Whether you’re trying to unravel the mystery of a Japanese sign or want to know what the hell that road sign in Iceland means, the instant translation offered by some AI apps is your best friend.Cultural learning beyond wordsSome of these apps don’t just translate words for you; they immerse you in a pool of culture without the need for floats. Think of them as a bridge between you and the authentic native experiences that await you in every corner of the world.Suddenly you learn to say “thank you” in Italian so convincingly that even the “nonna” at the restaurant smiles at you.There are tools that not only teach you to speak like a native, but to understand their gestures, their jokes, and even prepare you to be the “King of Karaoke in Korea”.Gain independence and be the boss of your own trip.Need a tour guide? No way! With an AI translation app in your pocket, you become the hero of your own travel odyssey.These digital wonders give you the freedom to control your adventure, allowing you to discover those secret corners of Paris or navigate the back streets of Tokyo without becoming part of the scenery.They are your golden ticket to freedom, giving you the power to explore at your leisure without having to follow the pack like a duck in a line.It’s time to take the reins, blaze your own trail, and collect the epic stories everyone wants to hear.With these apps, independence isn’t just a word; it’s your new way of traveling.Improve your dining experienceHave you ever felt like a detective trying to solve the mystery of a foreign menu? With AI translation apps, the mystery is solved instantly.Imagine pointing your phone at a dish called “Risotto ai Funghi” and discovering that you’re not ordering a strange dessert, but a delicious rice with mushrooms.These apps are your personal Michelin guide, ensuring that every bite is an adventure for your taste buds and not an unwanted surprise.Makes using public transportation easierSay goodbye to the complicated signs and misunderstandings that get you around town.It’s like every traffic sign and schedule speaks your language, giving you a VIP pass to move around the city like a fish in water, ready to explain that the train leaves in 5 minutes, not 50.Suddenly, getting from point A to point B is as easy as ordering a pizza.Improve your personal safetyIn a pinch, these apps become your capeless hero. Whether it’s explaining a shellfish allergy or locating the nearest emergency exit, they help you communicate clearly and avoid those “lost in translation” moments no one wants to experience.Access real-time local information:See that poster about a local event? Yeah, the one that looks interesting but is in a language you don’t understand.With a quick scan, your translation app tells you all about that secret concert or food festival that only the locals go to.Congratulations! You’ve just upgraded your status from tourist to expert traveler.Flexibility and convenienceWant to change your plans and venture to a nearby town recommended by a local you met yesterday at the train station? Of course you can!With the confidence your translation app gives you, you can decide to follow that spontaneous advice and visit a nearby town without worrying about the language. Your trip, your rules.Choosing the best translation app for your travelsWhen choosing a translation app, it is important to consider the variety of languages available, the accuracy of the translation, and the additional features it offers.LIKE.TG apps, for example, stand out for their wide range of supported languages and innovative features that go beyond simple translation, such as real-time speech recognition and built-in language lessons.REMEMBER !!!You can downloadour available appsfor translating and learning languages correctly available for free on googleplay and applestores.Do not hesitate to visit ourLIKE.TG websiteand contact us with any questions or problems you may have, and of course, take a look at any ofour blog articles.
AI-based translation tools: Analysis and comparison of the best ones
AI-based translation tools
Analysis and comparison of the best ones
As globalization increases, companies and individuals are finding it necessary to communicate more frequently with people who speak different languages.As a result, the need for translation tools has become more pressing.The good news is that there are now AI-based translation tools that make the process of translating text and speech faster and more accurate than ever before.In this article, I will analyze and compare the best AI-based translation tools available, discussing their advantages, features and drawbacks.Introduction to AI-based translation toolsAI-based translation tools use artificial intelligence to translate text and speech from one language to another. These tools have become increasingly popular in recent years thanks to advances in machine learning and natural language processing. Such tools are faster, more accurate and can handle a higher volume of work.Benefits of using AI-based translation toolsOne of the main advantages of using AI-based translation tools is speed. These tools can translate large volumes of text in a matter of seconds, whereas it would take a human translator much longer to do the same job.They are less likely to make mistakes and can also be used to translate speeches in real time, which makes them very useful for international conferences or business meetings.Popular AI-based translation tools and their featuresThere are many AI-based translation tools, each with its own unique features. Here are some of the most popular ones and what they offer:1. Google TranslateGoogle Translate is one of the most well-known AI-based translation tools. It offers translations in over 100 languages and can be used to translate text, speech, and even images. Google Translate also offers a feature called “Conversation Mode,” which allows two people to have a conversation in different languages using the same device.2. Microsoft TranslatorMicrosoft Translator is another popular AI-based translation tool. It offers translations in over 60 languages and can be used to translate text, speech, and images. Microsoft Translator also offers a feature called “Live Feature,” which allows two people to have a conversation in different languages using their own devices.3. DeepLDeepL is a newer AI-based translation tool, but it has quickly gained popularity thanks to its high-quality translations. It offers translations in nine languages and can be used to translate text. DeepL uses deep learning algorithms to produce translations that are more accurate and natural-sounding than those produced by other translation tools.4. LIKE.TG TranslateLIKE.TG Translate is a relatively new AI-based translation tool that has gained popularity in recent years. It is available in over 125 languages and can translate text, voice and images. One of the unique features of LIKE.TG Translate is its ability to translate text within other apps.The best feature of these apps is that not only do they base their translation using AI but they have a team of native translators behind them constantly improving their applications to make them even better.Factors to consider when choosing an AI-based translation toolWhen choosing an AI-based translation tool, there are several factors to consider. The first is the languages you need to translate. Make sure the tool you choose supports the languages you need. The second factor is the type of translations you need. Do you need to translate text, speech, or images? Do you need real-time translation for conversations? The third factor is the accuracy of the translations. Consider the quality of the translations produced by each tool. Lastly, consider the cost of the tool. Some AI-based translation tools are free, while others require a subscription or payment per use.Pros and cons of using AI-based translation toolsLike any tool, AI-based translation tools have pros and cons. Here are some of the main advantages and drawbacks of using these tools:After a thorough analysis, I can faithfully describe to you some of the most characteristic pros and cons of these tools:PROSAccuracy: These tools are able to better understand the context and syntax of the language, which translates into greater translation accuracy.Speed: Translating large amounts of text can take a long time if done manually, whereas AI-based translation tools are able to process large amounts of text in a matter of seconds.Cost savings: AI-based translation tools are often less expensive than human translation services, especially for large projects.Integrations: Many of these tools integrate with other platforms and productivity tools, making them easy to use in different contexts.CONSLack of context: These tools often lack context, which can result in inaccurate or inconsistent translations. For example, a literal translation of a sentence in one language into another may not take into account cultural connotations or social context and result in a translation that makes no sense.Lack of accuracy: Although AI-based translation tools have improved significantly in recent years, they are still not as accurate as humans. Translations can be inaccurate or have grammatical and spelling errors, especially in more complex or technical languages.They cannot capture nuances or tones: Such translation tools cannot capture nuances or tones that are often important in human communication. For example, they may miss the sarcastic or ironic tone of a sentence and translate it literally.Language dependency: language dependent, meaning that they work best for translating between widely spoken and documented languages but do not represent less common languages or regional dialects well. .Cost: While there are some available for free, many of the high-quality tools are quite expensive.Lack of customization: AI-based translation tools cannot be customized to meet the specific needs of an individual or company. This can limit their usefulness especially when highly specialized or technical translation is required.Privacy and security: Some tools collect and store sensitive data, which can raise serious concerns about data privacy and security.In conclusion, AI-based translation tools offer a number of advantages in terms of speed, accuracy and cost, but it is important to be aware of their limitations and challenges when selecting a tool.How AI-based translation tools are changing the translation industryAI-based translation tools are changing the translation industry in several ways. The first is that the translation process is faster and more efficient. This allows translators to handle larger volumes of work and deliver projects faster. The second way in which they are changing the industry is that specialized translators are becoming more in demand, as human quality is irreplaceable and although they can do basic translations, they have problems with technical or specialized language.This means that specialized translators in certain areas are more in demand than ever.The future of AI-based translation toolsThe future of AI-based translation tools is bright. As technology continues to advance, these tools will become even more sophisticated and accurate. We may eventually see a tool capable of handling all forms of language, including slang and regional dialects. It is also possible that they will become more integrated into our daily lives, allowing us to communicate with people who speak different languages more easily than ever before, yet experts continue to warn that humans cannot be replaced.Conclusion and recommendations for the best AI-based translation toolsIn conclusion, AI-based translation tools offer many advantages over traditional methods. They are faster, more accurate and can handle a higher volume of work. However, it is important to consider the languages you need to translate, the type of translations you need, the accuracy of the translations and the cost of the tool when choosing an AI-based translation tool, because at the end of the day no AI can replace a human being, nor can it emulate the human quality that a human being can bring to us.Based on our analysis and comparison, we recommend Google Translate for its versatility and variety of features. However, if you need high quality translations, LIKE.TG Translate may be the best choice.REMEMBER !!!You can downloadour available appsfor translating and learning languages correctly available for free on googleplay and applestores.Do not hesitate to visit ourLIKE.TG websiteand contact us with any questions or problems you may have, and of course, take a look at any ofour blog articles.
Artificial intelligence (AI) in language teaching: Future perspectives and challenges
Artificial intelligence (AI) in language teaching
Future perspectives and challenges
In a world where educational technology is advancing by leaps and bounds, it is no surprise that artificial intelligence is revolutionizing the way we learn languages.The combination of machine learning in education and AI in language teaching has opened up a range of exciting possibilities and, at the same time, poses challenges that we must face to make the most of this innovation.What is Artificial Intelligence in Language Teaching?Artificial intelligence (AI) in language teaching refers to the use of algorithms and computer systems to facilitate the process of learning a new language.From mobile apps to online platforms, AI has been integrated into a variety of tools designed to help students improve their language skills efficiently and effectively.Advances in AI and its challenges in language learningArtificial intelligence (AI) is radically transforming the way we learn languages. With the emergence of AI-powered apps and platforms, students have access to innovative tools that personalize learning to their individual needs.These tools use machine learning algorithms to analyze student progress and deliver tailored content, from grammar exercises to conversation practice.Additionally, AI-powered translation has significantly improved in accuracy and speed. Apps like LIKE.TG Translate allow users to instantly translate between multiple languages ​​with just a few clicks, making multilingual communication easier.Artificial Intelligence offers unprecedented potential to improve the language learning process, providing students with personalized and efficient tools.Positive Perspectives of AI in Language TeachingOne of the main advantages of AI in language teaching is its ability to personalize learning. Through data analysis and machine learning, AI systems can adapt digital learning platforms, content and activities based on the needs and preferences of each student.This allows for a more individualized and effective approach to improving language skills.In addition, AI has also enabled the development of more accurate and faster real-time translation tools. With apps like LIKE.TG Translate, users can access instant translations in multiple languages ​​with just a few clicks.This facilitates communication in multilingual environments and expands opportunities for interaction and learning.AI in language teaching opens the doors to global communication without barriersChallenges and Future ChallengesDespite advances in AI applied to language teaching, there are still important challenges that we must overcome. One of the main challenges is to guarantee the quality and accuracy of the content generated by AI.While AI systems can be effective in providing feedback and practice exercises, there are still areas where human intervention is necessary to correct errors and provide high-quality teaching.Another important challenge is ensuring that AI in language teaching is accessible to everyone. As we move towards an increasingly digitalized future, it is crucial to ensure that all people, regardless of their geographic location or socioeconomic status, have access to AI language learning apps.This will require investment in technological infrastructure and digital literacy programs around the world.How Long Is It Possible to Learn a Language with Artificial Intelligence?With the help of artificial intelligence (AI), learning a new language can be more efficient than ever.Although the time required to master a language varies depending on various factors, such as the complexity of the language, the level of dedication of the learner, and the quality of the AI ​​tools used, many people have managed to acquire significant language skills in a relatively short period of time.Thanks to AI applications and platforms designed specifically for language learning, users can benefit from a personalized approach tailored to their individual needs.These tools use machine learning algorithms to identify areas for improvement and provide relevant content, speeding up the learning process.On average, some people have reported significant gains in their language proficiency in just a few months of consistent use of AI tools.However, it is important to keep in mind that learning a language is an ongoing process and that completing mastery can take years of constant practice and exposure to the language in real-world contexts.Ultimately, the time needed to learn a language with AI depends largely on the commitment and dedication of the student.“The journey to mastering a language with AI begins with small daily steps, but constant dedication is the key to achieving the desired fluency.”In conclusion, the integration of technology in education and artificial intelligence in language teaching offers exciting opportunities to improve the learning process and promote intercultural global communication.However, it also poses challenges that we must proactively address to ensure that everyone can benefit from this innovation in education.With a collaborative approach and a continued commitment to educational excellence, we can fully realize the potential of AI in language teaching and prepare for a multilingual and globalized future.Visit our website for more information and begin your journey towards mastering languages ​​​​with the best and most advanced technology.
海外工具
10个最好的网站数据实时分析工具
10个最好的网站数据实时分析工具
网络分析工具可以帮助你收集、预估和分析网站的访问记录,对于网站优化、市场研究来说,是个非常实用的工具。每一个网站开发者和所有者,想知道他的网站的完整的状态和访问信息,目前互联网中有很多分析工具,本文选取了20款最好的分析工具,可以为你提供实时访问数据。1.Google Analytics这是一个使用最广泛的访问统计分析工具,几周前,Google Analytics推出了一项新功能,可以提供实时报告。你可以看到你的网站中目前在线的访客数量,了解他们观看了哪些网页、他们通过哪个网站链接到你的网站、来自哪个国家等等。2. Clicky与Google Analytics这种庞大的分析系统相比,Clicky相对比较简易,它在控制面板上描供了一系列统计数据,包括最近三天的访问量、最高的20个链接来源及最高20个关键字,虽说数据种类不多,但可直观的反映出当前站点的访问情况,而且UI也比较简洁清新。3. WoopraWoopra将实时统计带到了另一个层次,它能实时直播网站的访问数据,你甚至可以使用Woopra Chat部件与用户聊天。它还拥有先进的通知功能,可让你建立各类通知,如电子邮件、声音、弹出框等。4. Chartbeat这是针对新闻出版和其他类型网站的实时分析工具。针对电子商务网站的专业分析功能即将推出。它可以让你查看访问者如何与你的网站进行互动,这可以帮助你改善你的网站。5. GoSquared它提供了所有常用的分析功能,并且还可以让你查看特定访客的数据。它集成了Olark,可以让你与访客进行聊天。6. Mixpane该工具可以让你查看访客数据,并分析趋势,以及比较几天内的变化情况。7. Reinvigorate它提供了所有常用的实时分析功能,可以让你直观地了解访客点击了哪些地方。你甚至可以查看注册用户的名称标签,这样你就可以跟踪他们对网站的使用情况了。8. Piwi这是一个开源的实时分析工具,你可以轻松下载并安装在自己的服务器上。9. ShinyStat该网站提供了四种产品,其中包括一个有限制的免费分析产品,可用于个人和非营利网站。企业版拥有搜索引擎排名检测,可以帮助你跟踪和改善网站的排名。10. StatCounter这是一个免费的实时分析工具,只需几行代码即可安装。它提供了所有常用的分析数据,此外,你还可以设置每天、每周或每月自动给你发送电子邮件报告。本文转载自:https://www.cifnews.com/search/article?keyword=工具
10款常用的SEO内容优化工具
10款常用的SEO内容优化工具
谷歌使用含有数百个加权因子的复杂算法,根据给定网页与给定关键词的相关性,对网页进行索引和排名。数字营销人员则通过实证测试试图弄清这个复杂算法背后的原理,并采用特定的方法来提高网页在搜索结果页中的排名,这一过程被叫做搜索引擎优化(SEO),这是数字营销人员必须掌握的重要技能。 如果没有优质SEO内容工具,优化网页内容将是一项冗长乏味的工作。为了帮助您节省大量时间和劳动力,本为会为您推荐10个最佳SEO内容创作工具,这些工具适用于内容创作过程的不同阶段。 1. Google Search Console 价格:网站所有者可免费使用 作用:Google Search Console是谷歌自己的工具,能够帮助提高网站在搜索引擎结果页面中的排名。它包括网站性能监视工具,页面加载时间监视工具。您还可以监控您的网站在Google搜索结果中的排名,了解哪些页面是针对特定关键词进行排名的。您还可以查看网页在搜索结果页面的展示次数和点击次数。它帮助您确定该优化哪些内容,以及接下来该定位哪些关键词。 2. Google Keyword Planner 价格:拥有Google Ads账户的人均可免费使用 作用:Google Keyword Planner是进行基本的关键词研究的最佳免费工具之一。您可以 1)发现新关键词:输入任何关键词来查看与其类似的关键词列表,以及它们的搜索量和相关指标,使得你很容易找到新的关键字优化目标;2)预测关键词趋势:监控趋势,以发现流行的搜索关键词。Kenny觉得这个工具只适合做SEM的小伙伴,如果你是做SEO的,那查找到的关键词数据不适合SEO。 3. WordStream 价格:免费 作用:WordStream 提供了一个精简版的Google Keyword Planner,它是免费的,易于使用。只需输入您选择的关键词,选择一个行业,并输入您的位置,然后单击Email All My Keywords按钮,您就可以获得关键词列表和它们在Google和Bing上的搜索量,以及每个关键词的平均每次点击成本(CPC) 4. SEMrush 价格:部分功能免费,订阅制99.95美元/月 作用:SEMrush 是最流行的工具之一,适用于所有类型的数字营销人员。它包含40多种不同的工具,可以帮助进行SEO、PPC和社交媒体管理。营销人员可以使用SEMrush分析反向链接、进行关键词研究、分析自己或竞争对手的网站性能和流量,并发现新的市场和机会。SEMrush还有一个SEO审计程序,可以帮助解决网站SEO的一些技术问题。 图片来源:SEMrush 5. BuzzSumo 价格:79美元/月 作用:BuzzSumo帮助营销人员有效分析网站内容,同时紧跟热门趋势。BuzzSumo能够找到用户在不同平台上最喜欢分享的内容。只需要输入网站链接,就能查看什么是该网站最热门的内容。您还可以分析过去一天内,一个月内以及一年内的趋势,并且按照作者或者平台过滤。 6. Answer the Public 价格:每天3次免费使用,无限使用99美元/月 作用:输入某一关键词,您可以查找到任何与之相联系的关键词,并获得可视化报告。这些关键字以您输入的关键词为中心,形成一个网状结构,展示它们之间的联系。借助Answer the Public,营销人员可以撰写针对性强的文章,使网页更有可能出现在Google Snippets中。 图片来源:Answer the Public 7. Yoast SEO 价格:基础版免费,高级版89美元/月 作用:Yoast SEO是一个WordPress插件。它可在您使用WordPress优化博客文章时,为您提供实时反馈,提供改进建议。它类似一个清单工具,实时告诉你撰写网站博文时还可以做哪些事来优化SEO。 8. Keyword Density Checker 价格:每月500次使用限制,如需解锁更多使用次数,可购买50美元/年的高级版 作用:关键字密度(Keyword density)是谷歌等搜索引擎用来对网页进行排名的重要因素。您应该确保目标关键词在每篇文章中被提到足够多的次数,同时还不能滥用关键词。keyword density checker可以计算出每个关键词在您的文章中被提及的次数。只要复制粘贴文本,您就能知道文章中出现频率最高的关键词列表。对于大多数内容而言,目标关键字的密度最好在2%到5%。 图片来源:Keyword Density Checker 9. Read-Able 价格:免费版可供使用,付费版4美元/月 作用:据统计,北美人的平均阅读水平在八年级左右。因此,如果北美人是您的目标受众,您应该撰写清晰易懂的句子和文章。如果您的目标受众受过大学教育,则可以使用较长的单词和复杂的句子。Read-able帮助您将文章写作水平与目标受众的阅读水平相匹配,为读者提供最佳体验。它提供阅读水平检查,语法和拼写检查等功能。 10. Grammarly Premium 价格:11.66美元/月 作用:搜索引擎将网站的拼写和语法纳入排名范围。如果网站内容包含许多拼写错误,它就不太可能获得一个高排名。Grammarly可以轻松创建语法正确且没有拼写错误的内容。您可以将Grammarly作为插件添加到浏览器,并在撰写电子邮件、社交媒体更新或博客文章时使用它。 从关键词研究到拼写检查和语法纠正,这10种工具涵盖了网站内容创建的每一个步骤。我们希望您在为网站编写内容时,可以使用其中一部分工具来节省时间和精力。如果您在实操上遇到困难,或者需要专业的咨询服务,一个专业的数字营销团队正是您需要的!Ara Analytics有丰富的搜索引擎优化经验,欢迎联系我们,我们将为您提供定制化的专业服务。 往期推荐: 支招!新网站引流SEO优化该怎么做? 十七招教你快速提升网站流量 | Google “SEO到底多久才可以见效啊?”-跨境电商提高自然流量必须知道的五个真相 【Google SEO】12款常用的免费谷歌SEO工具推荐- 助网站流量翻倍增长 (来源:Kenny出海推广) 以上内容属作者个人观点,不代表LIKE.TG立场!本文经原作者授权转载,转载需经原作者授权同意。​ 本文转载自:https://www.cifnews.com/search/article?keyword=工具
11大亚马逊数据工具,好用到尖叫!(黑五网一特惠福利)
11大亚马逊数据工具,好用到尖叫!(黑五网一特惠福利)
平台商家想要销量好,关键要选择有针对性的数据工具。本文将分享11款相关产品,帮助国内亚马逊卖家更好地解决日常销售中的问题。 这些工具可以帮助卖家找到一定需求的利基市场以及热销产品。 废话不多说,接着往下看吧! 1、 AmzChart (图片来源:AmzChart) AmzChart中的Amazon BSR图表工具涵盖9个国家,拥有超过数十万的产品分析。 如果你想在竞争中脱颖而出赢得竞品的市场份额,为企业带来财富的话,那么选择AmzChart准没错! 你可以选择AmzChart的理由: • Amazon BSR中可找到低竞争利基产品,助力销量增长至200%。 • 短短一分钟之内即可找到热销品类,帮助卖家深入更大的利润空间。 • 追踪竞争对手产品数据,并以电子邮件形式提供反馈。 • 反查对手ASIN功能可帮助商家分析竞争对手的关键词。 • 跟踪竞争对手的各项平台指标。 • 获取产品价格趋势,且可以轻松下载历史跟踪器插件,并安装自己的网站上。 • 通过分析报告和视频教程获得专业指导——在亚马逊经商之旅的各个阶段,你都不会孤立无援。 【点击此处】获取黑五网一福利:前3个月享5折优惠 2、 Jungle Scout (图片来源:Jungle Scout) 无论你是新手商家,或是已有经验的亚马逊老司机,Jungle Scout均可为你提供多方支持。 你可以选择Jungle Scout的理由: • 可使用筛选器从产品数据库中找到热销产品,快速又方便。 • 平台新手可通过量化数据做出决策,轻松推出产品。 • Jungel Scout可帮助商家精简业务流程,提高市场洞察能力。 • 大量的功能,如排名跟踪、listing搭建器、评价自动化、库存监管等。 3、Seller Labs Pro (图片来源:SellerLabs) 作为亚马逊智能关键字工具之一,SellerLabs能帮助商家提高自然排名和付费流量,以及一系列广泛工具。 无论是长尾关键词,还是PPC术语,你在这个工具中找到。专业版每个月49美元起价。年度计划更为划算,每月39美元起,共可节省120美元。 你可以选择Seller Labs Pro的理由: • 商家随时可监控流量、广告支出、转化率和下载报告,并将收到重要指标的通知。 • 实时通知可以帮助商家做出决策,避免缺货。 • 基于AI智能,为构建SEO策略提供详细建议。 • 访问优化工具,抓取热销产品关键字,节省运营时间。 4、 Helium 10 (图片来源:Helium 10) 作为一体化的亚马逊数据工具,Helium 10可轻松助力平台商家拓展业务。 你可以选择Helium 10 的理由: • 数据库中有4.5亿条ASIN数据,可帮助商家更快地找到产品。更直观进行分析和利润估算,以验证产品是否能够成功打入市场。 • 您可以探索关键字研究,如单字、反查对手ASIN、后端和低竞争度短语。 • 数百个关键字无缝编写listing,并让排名更靠前。 • 内置的安全工具能够避免安全威胁。可以使用警报和更新轻松地管理您的业务。 • 分析可以帮助做出强有力的决策,形成更好的产品排名。 • 可以轻松使用PPC管理和自动化以促进业务增长。 【点击此处】获取黑五限时特惠:购买两个月Diamond钻石套餐可享受5折优惠并获得额外福利。 5、AmaSuite 5 (图片来源:AmaSuite 5) AmaSuite 5具有强大的新功能,其中包括可以在Mac和Windows双系统完形成无缝工作流的Research桌面软件。 通过AmaSuite 5工具套件,商家可以发现利好关键字和产品,从而在亚马逊上赚到一笔。 你可以选择AmaSuite 5的理由: • 使用Ama Product Analyzer,可以找到各个品类的畅销产品。 • 可以通过输入主要产品关键字找到类似款式的畅销产品。 • 通过提取产品评论获得自有品牌产品想法,并可分析产品特点和优势,确保完成无风险销售行为。 • 访问亚马逊销售课程奖金,并学习如何在亚马逊开展规模化销售业务。其中的分步指南事无巨细地给予商家运营指导。 6、AMZBase (图片来源:AMZBase) AMZBase是一个免费的谷歌浏览器插件,以帮助亚马逊商家正确地选品。 你可以选择AMZBase 的理由: • 帮助获取亚马逊产品ASIN编码与listing标题描述。 • 免费访问CamelCamelCamel、阿里巴巴、全球速卖通、eBay和谷歌搜索。 • 可通过自动计算FBA费用确定预期利润。 • 一站式即时搜索工具,搜索谷歌及阿里巴巴上的相关产品。 • 只需选择关键字即可立即搜索。 • 使用AMZBase前,请将谷歌浏览器升级至最新版本。 7、Unicorn Smasher (图片来源:Unicorn Smasher) Unicorn Smasher是AmzTracker旗下产品,可以节省商家在亚马逊上的选品时间,帮助卖家更好地了解亚马逊上各个产品的定价、排名、评论和销售额。 你可以选择Unicorn Smasher的理由: • 简单、易操作的仪表盘界面,助力完成选品数据抓取。 • 根据亚马逊listing中的实时数据,获得每月的预估销售额。 • 保存商家或可节省511美元 8、Keepa (图片来源:Keepa) Keepa也是一个浏览器插件,也适用于其它所有主流浏览器。只需安装该插件,所有功能随即可全部免费使用。 你可以选择Keepa的理由: 一个免费的亚马逊产品搜索工具,具有深度数据筛选功能。 显示降价和可用性提醒的价格历史图表。 可在亚马逊上比较不同地区的价格。 可以依据价格高点下跌查询任一品类的近期交易。 可通过通知和愿望列表来进行数据跟踪。 9、ASINspector (图片来源:ASINspector) ASINspector是一个免费的谷歌插件,助力商家成为亚马逊上的专业人士。该工具不仅可以抓取利好产品信息,还能让商家以低价拿下供应商,从而获得较大利润。 你可以选择ASINspector的理由: 可提供预估销售和实时利润情况等数据。 使用AccuSales™数据分析引擎可节省选品时间。 挖掘利好产品想法,并可以红色、绿色和黄色进行标记。 用利润计算器查看决定产品是否存在合理利润空间。 与任一国家的任一亚马逊平台无缝衔接。 10、AMZScout AMZScout是卖家常用的亚马逊工具之一。 你可以选择AMZScout的理由: 访问产品数据库,查找热门新产品。 通过AMZSscout提供的培训课程提高销售技巧。 在任何国家/地区搜索国际供应商并以建立自己的品牌。 监控竞争对手的关键字、销售、定价等。 只需点击3次即可轻松安装,有中文版。 黑五福利:三五折优惠获完整工具集合,可节省511美元【点击此处】 11、 PickFu PickFu是一款亚马逊A/B测试工具,也是一个可以获取消费者问卷调查的平台。 你可以选择PickFu的理由: • 真实的美国消费者反馈 • 几分钟即可在线完成问卷调研 • 商品设计、图片、描述等及时反馈 • 精准的目标群众和属性划分 • 中文客服支持 【点击此处】获取网一福利:预购积分享8折 这11大效率型亚马逊工具已介绍完毕,相信你已经有了心仪的选择了!快去实践一下,试试看吧! (来源:AMZ实战) 以上内容仅代表作者本人观点,不代表LIKE.TG立场!如有关于作品内容、版权或其它问题请于作品发表后的30日内与LIKE.TG取得联系。 *上述文章存在营销推广内容(广告)本文转载自:https://www.cifnews.com/search/article?keyword=工具
全球峰会
1-4月美国电商支出3316亿美元,消费者转向低价商品
1-4月美国电商支出3316亿美元,消费者转向低价商品
AMZ123 获悉,日前,据外媒报道,Adobe Analytics 的数据显示,2024 年前四个月美国电商增长强劲,同比增长 7%,达到 3316 亿美元。据了解,Adobe Analytics 对美国在线交易数据进行了分析,涵盖美国零售网站的一万亿次访问、1 亿个 SKU 和 18 个产品类别。2024 年 1 月 1 日至 4 月 30 日,美国在线支出达 3316 亿美元,同比增长 7%,得益于电子产品、服装等非必需品的稳定支出以及在线杂货购物的持续激增。Adobe 预计,2024 年上半年在线支出将超过 5000 亿美元,同比增长 6.8%。今年前四个月,美国消费者在线上消费电子产品 618 亿美元(同比增长 3.1%),服装 525 亿美元(同比增长 2.6%)。尽管增幅较小,但这两个类别占电商总支出的 34.5%,帮助保持了营收增长。同时,杂货进一步推动了增长,在线支出达 388 亿美元,同比增长 15.7%。Adobe 预计,未来三年内,该类别将成为电商市场的主导力量,其收入份额与电子产品和服装相当。另一个在线支出费增长较快的类别是化妆品,该类别在 2023 年带来了 350 亿美元的在线消费,同比增长 15.6%。而这一上升趋势仍在继续,截至 4 月 30 日,2024 年美国消费者在化妆品上的在线支出为 132 亿美元,同比增长 8%。此外,数月持续的通货膨胀导致消费者在多个主要类别中购买更便宜的商品。Adobe 发现,个人护理(增长 96%)、电子产品(增长 64%)、服装(增长 47%)、家居/花园(增长 42%)、家具/床上用品(增长 42%)和杂货(增长 33%)等类别的低价商品份额均大幅增加。具体而言,在食品杂货等类别中,低通胀商品的收入增长 13.4%,而高通胀商品的收入下降 15.6%。在化妆品等类别中,影响相对较弱,低通胀商品的收入增长 3.06%,高通胀商品的收入仅下降 0.34%,主要由于消费者对自己喜欢的品牌表现出了更强的忠诚度。而体育用品(增长 28%)、家电(增长 26%)、工具/家装(增长 26%)和玩具(增长 25%)等类别的低价商品份额增幅均较小,这些类别的增幅也主要受品牌忠诚度影响,同时消费者更倾向于购买最高品质的此类产品。此外,“先买后付”(BNPL)支付方式在此期间也出现了持续增长。2024 年 1 月至 4 月,BNPL 推动了 259 亿美元的电商支出,较去年同期大幅增长 11.8%。Adobe 预计,BNPL 将在 2024 年全年推动 810 亿至 848 亿美元的支出,同比增长 8% 至 13%。
12月波兰社媒平台流量盘点,TikTok追赶Instagram
12月波兰社媒平台流量盘点,TikTok追赶Instagram
AMZ123 获悉,近日,市场分析机构 Mediapanel 公布了 2023 年 12 月波兰主流社交平台的最新用户统计数据。受 TikTok 的打击,Pinterest、Facebook 和 Instagram 的用户数量出现下降。根据 Mediapanel 的数据,截至 2023 年 12 月,TikTok 是波兰第三大社交媒体平台,拥有超过 1378 万用户,相当于波兰 46.45% 的互联网用户。排在 TikTok 之前的是 Facebook 和 Instagram,其中 Facebook 拥有超过 2435 万用户,相当于波兰 82.06% 的互联网用户;Instagram 则拥有超过 1409 万用户,相当于波兰 47.47% 的互联网用户。在用户使用时长方面,TikTok 排名第一。2023 年 12 月,TikTok 用户的平均使用时长为 17 小时 18 分钟 42 秒。Facebook 用户的平均使用时长为 15 小时 36 分钟 38 秒,位居第二。其次是 Instagram,平均使用时长为 5 小时 2 分钟 39 秒。与 11 月相比,12 月 Facebook 减少了 58.84 万用户(下降 2.4%),但其用户平均使用时间增加了 32 分钟 50 秒(增长 3.6%)。Instagram 流失了 25.9 万用户(下降 1.8%),但其用户平均使用时间增加了 15 分钟(增长 5.2%)。虽然 TikTok 的用户数量略有增长(增长 8.85 万,即 0.6%),但其用户平均使用时间减少了 47 分钟(减少 4.3%)。12 月份,波兰其他主流社交媒体平台的用户数据(与 11 月相比):X 增加了 39.64 万用户(增长 4.8%),用户平均使用时间增加了 6 分钟 19 秒(增长 9.3%);Pinterest 增加了 23.02 万用户(增长 3.5%),用户平均使用时间增加了 7 分钟 9 秒(增长 16.1%);Snapchat 则增加了 9.04 万用户(增长 1.8%),用户平均使用时间增加了 23 秒(增长 0.2%);LinkedIn 流失了 27.69 万用户(下降 6.2%),用户平均使用时间减少了 1 分钟 36 秒(下降 11.7%);Reddit 流失了 18.6 万用户(下降 7.1%),用户平均使用时间减少了 1 分钟 27 秒(下降 11.6%)。
178W应用、3700W注册开发者,图表详解苹果首个App Store数据透明度报告
178W应用、3700W注册开发者,图表详解苹果首个App Store数据透明度报告
近日,苹果发布 2022 年 App Store 透明度报告,展示了 App Store 在 175 个国家和地区运营的数据,包括在线/下架应用数量、提审被拒应用数量、每周访问量、搜索量等。为帮助开发者快速了解 App Store 新发布的各项数据情况,在本篇内容中,AppStare 拆解了各项数据,为开发者提供直观展示,可供参考。app 数据App Store 在线及下架 app 数量报告显示,2022 年,App Store 中在线 app 总数量超 178 万(1,783,232),从 App Store 下架的 app 数量超 18 万(186,195)。提交审核及被拒的 app 数量共有超 610 万(6,101,913)款 app 提交到 App Store 进行审核,其中近 168 万(1,679,694)款 app 提审被拒,占比 27.53%,审核拒绝的主要原因包括性能问题、违反当地法律、不符合设计规范等。此外,提审被拒后再次提交并通过审核的 app 数量超 25 万(253,466),占比 15.09%。不同原因提审被拒的 app 数量app 提审被 App Store 审核指南拒绝的原因包括 app 性能问题、违反当地法律、不符合设计规范、业务问题、存在安全风险及其他六大模块。从上图可见,性能问题是 app 提审被拒的最大原因,超 101 万(1,018,415)款 app 因此被 App Store 审核指南拒绝,占比达 50.98%。建议开发者在 app 提审前,针对 App Store 审核指南再做详细的自我审查,提升通过可能。从 App Store 下架的 app Top 10 分类2022 年,App Store 下架超 18 万(186,195)款 app,其中游戏类 app 是下架次数最多的应用类别,超 3.8 万(38,883)款,占比 20.88%,其次为 工具类 app,共下架 2 万(20,045)款,占比 10.77%。中国大陆下架 app 品类 top 10在中国大陆地区,下架 app 总计超 4 万(41,238)款。工具类 app 是下架数量最多的 app 子品类,达 9,077 款,占比 22.01%,其次为游戏类 app,下架 6,173 款,占比 14.97%。被下架后申诉的 app 数量在 175 个国家/地区中,被下架后申诉的 app 数量总计超 1.8 万(18,412)款。中国大陆下架后申诉的 app 数量最多,达 5,484 款,占比 29.78%。申诉后恢复上架的 app 数量申诉后恢复上架的 app 数量总计为 616 款,其中中国大陆申诉后恢复上架的 app 最多,为 169 款,占中国大陆下架后申诉 app 数量(5,484)的 3.08%。开发者数据注册苹果开发者总数近 3700 万(36,974,015),被终止开发者账户数量近 43 万(428,487),占比 1.16%。其中,开发者账户因违反开发者计划许可协议(DPLA)而被终止的主要原因分别有欺诈(428,249)、出口管制(238)等。被终止后申诉的开发者账户数量为 3,338,被终止后申诉并恢复的开发者账户数量为 159,占比 4.76%。用户数据在用户方面,平均每周访问 App Store 的用户数超 6.56 亿(656,739,889)。2022 年,App Store 终止用户账户数量超 2.82 亿(282,036,628)。值得注意的是,App Store 还阻止了金额超 $20.9亿($2,090,195,480)的欺诈交易。在用户 app 下载方面,平均每周下载 app 数量超 7.47 亿(747,873,877),平均每周重新下载 app 数量超 15.39 亿(1,539,274,266),是前者的 2 倍。因此,建议开发者多加重视对回访用户的唤醒,相关推广策略的制定可能起到较为理想的效果。在 app 更新方面,平均每周自动更新 app 数量超 408 亿(40,876,789,492),平均每周手动更新 app 数量超 5 亿(512,545,816)。可见,用户在 app 更新问题上更偏向依赖自动更新。搜索数据平均每周在 App Store 搜索的用户数超 3.73 亿(373,211,396),App Store 的高质流量有目共睹。在至少 1000 次搜索中出现在搜索结果前 10 名的 app 总数近 140 万(1,399,741),平均每周出现在至少 1000 次搜索结果前 10 名的 app 数量 近 20 万(197,430)。除了通过元数据优化等操作提升 app 的搜索排名外,Apple Search Ads 也是帮助开发者提升 app 曝光和下载的重要渠道。
全球大数据
   探索Discord注册的多重用途
探索Discord注册的多重用途
在当今数字化时代,社交网络平台是人们沟通、分享和互动的重要场所。而Discord作为一款功能强大的聊天和社交平台,正吸引着越来越多的用户。那么,Discord注册可以用来做什么呢?让我们来探索它的多重用途。 首先,通过Discord注册,您可以加入各种兴趣群组和社区,与志同道合的人分享共同的爱好和话题。不论是游戏、音乐、电影还是科技,Discord上有无数个群组等待着您的加入。您可以与其他成员交流、参与讨论、组织活动,结识新朋友并扩大自己的社交圈子。 其次,Discord注册也为个人用户和团队提供了一个协作和沟通的平台。无论您是在学校、工作场所还是志愿组织,Discord的群组和频道功能使得团队成员之间可以方便地分享文件、讨论项目、安排日程,并保持密切的联系。它的语音和视频通话功能还能让远程团队更好地协同工作,提高效率。 对于商业用途而言,Discord注册同样具有巨大潜力。许多品牌和企业已经认识到了Discord作为一个与年轻受众互动的渠道的重要性。通过创建自己的Discord服务器,您可以与客户和粉丝建立更紧密的联系,提供独家内容、产品促销和用户支持。Discord还提供了一些商业工具,如机器人和API,帮助您扩展功能并提供更好的用户体验。 总结起来,Discord注册不仅可以让您加入各种兴趣群组和社区,享受与志同道合的人交流的乐趣,还可以为个人用户和团队提供协作和沟通的平台。对于品牌和企业而言,Discord也提供了与受众互动、推广产品和提供用户支持的机会。所以,赶紧注册一个Discord账号吧,开启多重社交和商业可能性的大门! -->
  商海客discord群发软件:开启营销革命的利器
商海客discord群发软件
开启营销革命的利器
商海客discord群发软件作为一款前沿的营销工具,以其独特的特点和出色的功能,在商业领域掀起了一场营销革命。它不仅为企业带来了全新的营销方式,也为企业创造了巨大的商业价值。 首先,商海客discord群发软件以其高效的群发功能,打破了传统营销方式的束缚。传统营销常常面临信息传递效率低、覆盖范围有限的问题。而商海客discord群发软件通过其强大的群发功能,可以将信息迅速传递给大量的目标受众,实现广告的精准推送。不论是产品推广、品牌宣传还是促销活动,商海客discord群发软件都能帮助企业快速触达潜在客户,提高营销效果。 其次,商海客discord群发软件提供了丰富的营销工具和功能,为企业的营销活动增添了更多的可能性。商海客discord群发软件支持多种媒体形式的推送,包括文本、图片、音频和视频等。企业可以根据自身需求,定制个性化的消息内容和推广方案,以吸引目标受众的注意。此外,商海客discord群发软件还提供了数据分析和统计功能,帮助企业了解营销效果,进行精细化的调整和优化。 最后,商海客discord群发软件的用户体验和易用性也为企业带来了便利。商海客discord群发软件的界面简洁明了,操作简单易懂,即使对于非技术人员也能够快速上手。商海客discord群发软件还提供了稳定的技术支持和优质的客户服务,确保用户在使用过程中能够获得及时的帮助和解决问题。 -->
 Discord|海外社媒营销的下一个风口?
Discord|海外社媒营销的下一个风口?
Discord这个软件相信打游戏的各位多少都会有点了解。作为功能上和YY相类似的语音软件,已经逐渐成为各类游戏玩家的青睐。在这里你可以创建属于自己的频道,叫上三五个朋友一起开黑,体验线上五连坐的游戏体验。但Discord可不是我们口中说的美国版YY这么简单。 Discord最初是为了方便人们交流而创立的应用程序。游戏玩家、电影迷和美剧迷、包括NFT创作者和区块链项目都在Discord上装修起一个个属于自己的小家。而在互联网的不断发展中,Discord现如今已经发展成为一种高效的营销工具,其强大的社区的功能已远不止语音交谈这一单一功能了。本文我们将结合市场营销现有的一些概念,带你领略Discord背后的无穷价值。 初代海外社媒营销: 当我们谈及Marketing市场营销,我们大多能想到的就是广告,以广告投放去获得较为多的转化为最终目的。但随着公众利益的变化,市场营销的策略也在不断改变。社交媒体类别的营销是现在更多品牌更为看重的一块流量池。我们可以选择付费营销,当然也可以选择不付费,这正式大多数的品牌所处的阶段。如国内的微博,抖音。又好比海外的Facebook, Instagram等。 但是,当我们深入地了解这些社交媒体的算法时不难发现。人们经常会错过我们的内容,又或者在看到这是一个广告之后就选择离开,其推广的触达率并不显著。其原因其实和初代社交媒体的属性分不开。 我们来打个比方:当你在YouTube上看着喜爱的博主视频,YouTube突然暂停了你的视频,给你插入了品牌方的广告。试问你的心情如何?你会选择安心看完这个广告,对其推广的产品产生了兴趣。还是想尽一切办法去关掉这个烦人的广告?而在不付费的内容上:你更喜欢看那些能娱乐你,充实你生活的内容。还是选择去看一个可能和你毫不相干的品牌贴文?在大数据的加持下,品牌方可能绞尽脑汁的想去获得你这个用户。但选择权仍就在用户手上,用户选择社交媒体的原因更多是为了娱乐和社交。我们也不愿意和一个个客气的“品牌Logo”去对话。 Discord是如何改变营销世界的? Discord又有什么不一样呢?你觉的他的营销手段就像发Email一样,给你特定的社群发送一组消息?谈到Email,这里要插一嘴。其触达率表现也并不优异,你发送的重要通告,新闻稿,打折促销。都有可能在用户还未浏览收之前就已经进了垃圾箱,又或者是和其他数百封未读邮件中等待着缘分的到来。 其实Discord的频道属性很美妙的化解了社交媒体现在的窘境,我们再来打个比方:比如你很喜欢篮球,因此你进入到了这个Discord篮球频道。而在这个频道里又包含了中锋,前锋,后卫这些细分频道。后卫又细分到了控球后卫,得分后卫。但总的来说,这个频道的用户都是喜欢篮球的群体。Discord的属性也拉近了品牌和用户的距离,你们不再是用户和一个个官方的“品牌Logo”对话。取而代之的则是一个个亲近感十足的好兄弟。直播带货中的“家人们”好像就是这一形式哈哈。 因此在Discord 上你可以针对不同频道发送不同的公告消息,使目标用户能够及时获得你的任何更新。他可不像电子邮件一样,淹没在一堆未读邮件中,也不会像社媒贴文一样被忽视。更精准的去区分不同的目标受众这一独特性也注定了Discord Marketing的强大功能。 Discord拓展属性: 自Facebook更名Meta等一系列动作下,2021年被世人称为元宇宙元年。在这一大背景下,更多的社交媒体开始逐渐向元宇宙靠拢。Twitter逐渐成为各类项目方的首选宣发媒体。Discord的属性也被更多项目方所发现,现如今Discord已被广泛运用在区块链领域。Discord事实上已经成为加密货币社区的最大聚集地,学习使用Discord也已经成为了圈内最入门技能。随着未来大量的区块链项目的上线Discord也将获得更加直接的变现手段。 Discord的各类载体已经数不胜数,区块链、游戏开黑、公司办公软件、线上教课。Discord是否能成为海外社媒的下一个风口?还是他已经成为了?这个不是我们能说了算的,但甭管你是想做品牌推广,还是单纯的就想酣畅漓淋的和朋友一起开个黑。选择Discord都是一个不错的选择。 -->
社交媒体

                    100+ Instagram Stats You Need to Know in 2024
100+ Instagram Stats You Need to Know in 2024
It feels like Instagram, more than any other social media platform, is evolving at a dizzying pace. It can take a lot of work to keep up as it continues to roll out new features, updates, and algorithm changes. That‘s where the Instagram stats come in. There’s a lot of research about Instagram — everything from its users' demographics, brand adoption stats, and all the difference between micro and nano influencers. I use this data to inform my marketing strategies and benchmark my efforts. Read on to uncover more social media stats to help you get ideas and improve your Instagram posting strategy. 80+ Instagram Stats Click on a category below to jump to the stats for that category: Instagram's Growth Instagram User Demographics Brand Adoption Instagram Post Content Instagram Posting Strategy Instagram Influencer Marketing Statistics Instagram's Growth Usage 1. Instagram is expected to reach 1.44 billion users by 2025. (Statista) 2. The Instagram app currently has over 1.4 billion monthly active users. (Statista) 3. U.S. adults spend an average of 33.1 minutes per day on Instagram in 2024, a 3-minute increase from the year before. (Sprout Social) 4. Instagram ad revenue is anticipated to reach $59.61 billion in 2024. (Oberlo) 5. Instagram’s Threads has over 15 Million monthly active users. (eMarketer) 6. 53.7% of marketers plan to use Instagram reels for influencer marketing in 2024. (eMarketer) 7. 71% of marketers say Instagram is the platform they want to learn about most. (Skillademia) 8. There are an estimated 158.4 million Instagram users in the United States in 2024. (DemandSage) 9. As of January 2024, India has 362.9 million Instagram users, the largest Instagram audience in the world. (Statista) 10. As of January 2024, Instagram is the fourth most popular social media platform globally based on monthly active users. Facebook is first. YouTube and WhatsApp rank second and third. (Statista) https://youtu.be/EyHV8aZFWqg 11. Over 400 million Instagram users use the Stories feature daily. (Keyhole) 12. As of April 2024, the most-liked post on Instagram remains a carousel of Argentine footballer Lionel Messi and his teammates celebrating the 2022 FIFA World Cup win. (FIFA) 13. The fastest-growing content creator on Instagram in 2024 is influencer Danchmerk, who grew from 16k to 1.6 Million followers in 8 months. (Instagram) 14. The most-followed Instagram account as of March 2024 is professional soccer player Cristiano Ronaldo, with 672 million followers. (Forbes) 15. As of April 2024, Instagram’s own account has 627 million followers. (Instagram) Instagram User Demographics 16. Over half of the global Instagram population is 34 or younger. (Statista) 17. As of January 2024, almost 17% of global active Instagram users were men between 18 and 24. (Statista) 18. Instagram’s largest demographics are Millennials and Gen Z, comprising 61.8% of users in 2024. (MixBloom) 19. Instagram is Gen Z’s second most popular social media platform, with 75% of respondents claiming usage of the platform, after YouTube at 80%. (Later) 20. 37.74% of the world’s 5.3 billion active internet users regularly access Instagram. (Backlinko) 21. In January 2024, 55% of Instagram users in the United States were women, and 44% were men. (Statista) 22. Only 7% of Instagram users in the U.S. belong to the 13 to 17-year age group. (Statista) 23. Only 5.7% of Instagram users in the U.S. are 65+ as of 2024. (Statista) 24. Only 0.2% of Instagram users are unique to the platform. Most use Instagram alongside Facebook (80.8%), YouTube (77.4%), and TikTok (52.8%). (Sprout Social) 25. Instagram users lean slightly into higher tax brackets, with 47% claiming household income over $75,000. (Hootsuite) 26. Instagram users worldwide on Android devices spend an average of 29.7 minutes per day (14 hours 50 minutes per month) on the app. (Backlinko) 27. 73% of U.S. teens say Instagram is the best way for brands to reach them. (eMarketer) 28. 500 million+ accounts use Instagram Stories every day. (Facebook) 29. 35% of music listeners in the U.S. who follow artists on Facebook and Instagram do so to connect with other fans or feel like part of a community. (Facebook) 30. The average Instagram user spends 33 minutes a day on the app. (Oberlo) 31. 45% of people in urban areas use Instagram, while only 25% of people in rural areas use the app. (Backlinko) 32. Approximately 85% of Instagram’s user base is under the age of 45. (Statista) 33. As of January 2024, the largest age group on Instagram is 18-24 at 32%, followed by 30.6% between ages 25-34. (Statista) 34. Globally, the platform is nearly split down the middle in terms of gender, with 51.8% male and 48.2% female users. (Phyllo) 35. The numbers differ slightly in the U.S., with 56% of users aged 13+ being female and 44% male. (Backlinko) 36. As of January 2024, Instagram is most prevalent in India, with 358.55 million users, followed by the United States (158.45 million), Brazil (122.9 million), Indonesia (104.8 million), and Turkey (56.7 million). (Backlinko) 37. 49% of Instagram users are college graduates. (Hootsuite) 38. Over 1.628 Billion Instagram users are reachable via advertising. (DataReportal) 39. As of January 2024, 20.3% of people on Earth use Instagram. (DataReportal) Brand Adoption 40. Instagram is the top platform for influencer marketing, with 80.8% of marketers planning to use it in 2024. (Sprout Social) 41. 29% of marketers plan to invest the most in Instagram out of any social media platform in 2023. (Statista) 42. Regarding brand safety, 86% of marketers feel comfortable advertising on Instagram. (Upbeat Agency) 43. 24% of marketers plan to invest in Instagram, the most out of all social media platforms, in 2024. (LIKE.TG) 44. 70% of shopping enthusiasts turn to Instagram for product discovery. (Omnicore Agency) 45. Marketers saw the highest engagement rates on Instagram from any other platform in 2024. (Hootsuite) 46. 29% of marketers say Instagram is the easiest platform for working with influencers and creators. (Statista) 47. 68% of marketers reported that Instagram generates high levels of ROI. (LIKE.TG) 48. 21% of marketers reported that Instagram yielded the most significant ROI in 2024. (LIKE.TG) 49. 52% of marketers plan to increase their investment in Instagram in 2024. (LIKE.TG) 50. In 2024, 42% of marketers felt “very comfortable” advertising on Instagram, and 40% responded “somewhat comfortable.” (LIKE.TG) 51. Only 6% of marketers plan to decrease their investment in Instagram in 2024. (LIKE.TG) 52. 39% of marketers plan to leverage Instagram for the first time in 2024. (LIKE.TG) 53. 90% of people on Instagram follow at least one business. (Instagram) 54. 50% of Instagram users are more interested in a brand when they see ads for it on Instagram. (Instagram) 55. 18% of marketers believe that Instagram has the highest growth potential of all social apps in 2024. (LIKE.TG) 56. 1 in 4 marketers say Instagram provides the highest quality leads from any social media platform. (LIKE.TG) 57. Nearly a quarter of marketers (23%) say that Instagram results in the highest engagement levels for their brand compared to other platforms. (LIKE.TG) 58. 46% of marketers leverage Instagram Shops. Of the marketers who leverage Instagram Shops, 50% report high ROI. (LIKE.TG) 59. 41% of marketers leverage Instagram Live Shopping. Of the marketers who leverage Instagram Live Shopping, 51% report high ROI. (LIKE.TG) 60. Education and Health and Wellness industries experience the highest engagement rates. (Hootsuite) 61. 67% of users surveyed have “swiped up” on the links of branded Stories. (LIKE.TG) 62. 130 million Instagram accounts tap on a shopping post to learn more about products every month. (Omnicore Agency) Instagram Post Content 63. Engagement for static photos has decreased by 44% since 2019, when Reels debuted. (Later) 64. The average engagement rate for photo posts is .059%. (Social Pilot) 65. The average engagement rate for carousel posts is 1.26% (Social Pilot) 66. The average engagement rate for Reel posts is 1.23% (Social Pilot) 67. Marketers rank Instagram as the platform with the best in-app search capabilities. (LIKE.TG) 68. The most popular Instagram Reel is from Samsung and has over 1 billion views. (Lifestyle Asia) 69. Marketers rank Instagram as the platform with the most accurate algorithm, followed by Facebook. (LIKE.TG) 70. A third of marketers say Instagram offers the most significant ROI when selling products directly within the app. (LIKE.TG) 71. Instagram Reels with the highest engagement rates come from accounts with fewer than 5000 followers, with an average engagement rate of 3.79%. (Social Pilot) 72. A third of marketers say Instagram offers the best tools for selling products directly within the app. (LIKE.TG) 73. Over 100 million people watch Instagram Live every day. (Social Pilot) 74. 70% of users watch Instagram stories daily. (Social Pilot) 75. 50% of people prefer funny Instagram content, followed by creative and informative posts. (Statista) 76. Instagram Reels are the most popular post format for sharing via DMs. (Instagram) 77. 40% of Instagram users post stories daily. (Social Pilot) 78. An average image on Instagram gets 23% more engagement than one published on Facebook. (Business of Apps) 79. The most geo-tagged city in the world is Los Angeles, California, and the tagged location with the highest engagement is Coachella, California. (LIKE.TG) Instagram Posting Strategy 80. The best time to post on Instagram is between 7 a.m. and 9 a.m. on weekdays. (Social Pilot) 81. Posts with a tagged location result in 79% higher engagement than posts without a tagged location. (Social Pilot) 82. 20% of users surveyed post to Instagram Stories on their business account more than once a week. (LIKE.TG) 83. 44% of users surveyed use Instagram Stories to promote products or services. (LIKE.TG) 84. One-third of the most viewed Stories come from businesses. (LIKE.TG) 85. More than 25 million businesses use Instagram to reach and engage with audiences. (Omnicore Agency) 86. 69% of U.S. marketers plan to spend most of their influencer budget on Instagram. (Omnicore Agency) 87. The industry that had the highest cooperation efficiency with Instagram influencers was healthcare, where influencer posts were 4.2x more efficient than brand posts. (Emplifi) 88. Instagram is now the most popular social platform for following brands. (Marketing Charts) Instagram Influencer Marketing Statistics 89. Instagram is the top platform for influencer marketing, with 80.8% of marketers planning to use the platform for such purposes in 2024 (Oberlo) 90. Nano-influencers (1,000 to 10,000 followers) comprise most of Instagram’s influencer population, at 65.4%. (Statista) 91. Micro-influencers (10,000 to 50,000 followers) account for 27.73% (Socially Powerful) 92. Mid-tier influencers (50,000 to 500,000 followers) account for 6.38% (Socially Powerful) 93. Nano-influencers (1,000 to 10,000 followers) have the highest engagement rate at 5.6% (EmbedSocial) 94. Mega-influencers and celebrities with more than 1 million followers account for 0.23%. (EmbedSocial) 95. 77% of Instagram influencers are women. (WPBeginner) 96. 30% of markers say that Instagram is their top channel for ROI in influencer marketing (Socially Powerful) 97. 25% of sponsored posts on Instagram are related to fashion (Socially Powerful) 98. The size of the Instagram influencer marketing industry is expected to reach $22.2 billion by 2025. (Socially Powerful) 99. On average, Instagram influencers charge $418 for a sponsored post in 2024, approximately 15.17%​​​​​​​ higher than in 2023. (Collabstr) 100. Nano-influencers charge between $10-$100 per Instagram post. (ClearVoice) 101. Celebrities and macro influencers charge anywhere from $10,000 to over $1 million for a single Instagram post in 2024. (Shopify) 102. Brands can expect to earn $4.12 of earned media value for each $1 spent on Instagram influencer marketing. (Shopify) The landscape of Instagram is vast and ever-expanding. However, understanding these key statistics will ensure your Instagram strategy is well-guided and your marketing dollars are allocated for maximum ROI. There’s more than just Instagram out there, of course. So, download the free guide below for the latest Instagram and Social Media trends.

                    130 Instagram Influencers You Need To Know About in 2022
130 Instagram Influencers You Need To Know About in 2022
In 2021, marketers that used influencer marketing said the trend resulted in the highest ROI. In fact, marketers have seen such success from influencer marketing that 86% plan to continue investing the same amount or increase their investments in the trend in 2022. But, if you’ve never used an influencer before, the task can seem daunting — who’s truly the best advocate for your brand? Here, we’ve cultivated a list of the most popular influencers in every industry — just click on one of the links below and take a look at the top influencers that can help you take your business to the next level: Top Food Influencers on Instagram Top Travel Influencers on Instagram Top Fashion Style Influencers on Instagram Top Photography Influencers on Instagram Top Lifestyle Influencers on Instagram Top Design Influencers on Instagram Top Beauty Influencers on Instagram Top Sport Fitness Influencers on Instagram Top Influencers on Instagram Top Food Influencers on Instagram Jamie Oliver (9.1M followers) ladyironchef (620k followers) Megan Gilmore (188k followers) Ashrod (104k followers) David Chang (1.7M followers) Ida Frosk (299k followers) Lindsey Silverman Love (101k followers) Nick N. (60.5k followers) Molly Tavoletti (50.1k followers) Russ Crandall (39.1k followers) Dennis the Prescott (616k followers) The Pasta Queen (1.5M followers) Thalia Ho (121k followers) Molly Yeh (810k followers) C.R Tan (59.4k followers) Michaela Vais (1.2M followers) Nicole Cogan (212k followers) Minimalist Baker (2.1M followers) Yumna Jawad (3.4M followers) Top Travel Influencers on Instagram Annette White (100k followers) Matthew Karsten (140k followers) The Points Guy (668k followers) The Blonde Abroad (520k followers) Eric Stoen (330k followers) Kate McCulley (99k followers) The Planet D (203k followers) Andrew Evans (59.9k followers) Jack Morris (2.6M followers) Lauren Bullen (2.1M followers) The Bucket List Family (2.6M followers) Fat Girls Traveling (55K followers) Tara Milk Tea (1.3M followers) Top Fashion Style Influencers on Instagram Alexa Chung (5.2M followers) Julia Berolzheimer (1.3M followers) Johnny Cirillo (719K followers) Chiara Ferragni (27.2M followers) Jenn Im (1.7M followers) Ada Oguntodu (65.1k followers) Emma Hill (826k followers) Gregory DelliCarpini Jr. (141k followers) Nicolette Mason (216k followers) Majawyh (382k followers) Garance Doré (693k followers) Ines de la Fressange (477k followers) Madelynn Furlong (202k followers) Giovanna Engelbert (1.4M followers) Mariano Di Vaio (6.8M followers) Aimee Song (6.5M followers) Danielle Bernstein (2.9M followers) Gabi Gregg (910k followers) Top Photography Influencers on Instagram Benjamin Lowy (218k followers) Michael Yamashita (1.8M followers) Stacy Kranitz (101k followers) Jimmy Chin (3.2M followers) Gueorgui Pinkhassov (161k followers) Dustin Giallanza (5.2k followers) Lindsey Childs (31.4k followers) Edith W. Young (24.9k followers) Alyssa Rose (9.6k followers) Donjay (106k followers) Jeff Rose (80.1k followers) Pei Ketron (728k followers) Paul Nicklen (7.3M followers) Jack Harries (1.3M followers) İlhan Eroğlu (852k followers) Top Lifestyle Influencers on Instagram Jannid Olsson Delér (1.2 million followers) Oliver Proudlock (691k followers) Jeremy Jacobowitz (434k followers) Jay Caesar (327k followers) Jessie Chanes (329k followers) Laura Noltemeyer (251k followers) Adorian Deck (44.9k followers) Hind Deer (547k followers) Gloria Morales (146k followers) Kennedy Cymone (1.6M followers) Sydney Leroux Dwyer (1.1M followers) Joanna Stevens Gaines (13.6M followers) Lilly Singh (11.6M followers) Rosanna Pansino (4.4M followers) Top Design Influencers on Instagram Marie Kondo (4M followers) Ashley Stark Kenner (1.2M followers) Casa Chicks (275k followers) Paulina Jamborowicz (195k followers) Kasia Będzińska (218k followers) Jenni Kayne (500k followers) Will Taylor (344k followers) Studio McGee (3.3M followers) Mandi Gubler (207k followers) Natalie Myers (51.6k followers) Grace Bonney (840k followers) Saudah Saleem (25.3k followers) Niña Williams (196k followers) Top Beauty Influencers on Instagram Michelle Phan (1.9M followers) Shaaanxo (1.3M followers) Jeffree Star (13.7M followers) Kandee Johnson (2M followers) Manny Gutierrez (4M followers) Naomi Giannopoulos (6.2M followers) Samantha Ravndahl (2.1M followers) Huda Kattan (50.5M followers) Wayne Goss (703k followers) Zoe Sugg (9.3M followers) James Charles (22.9M followers) Shayla Mitchell (2.9M followers) Top Sport Fitness Influencers on Instagram Massy Arias (2.7M followers) Eddie Hall (3.3M followers) Ty Haney (92.6k followers) Hannah Bronfman (893k followers) Kenneth Gallarzo (331k followers) Elisabeth Akinwale (113k followers) Laura Large (75k followers) Akin Akman (82.3k followers) Sjana Elise Earp (1.4M followers) Cassey Ho (2.3M followers) Kayla Itsines (14.5M followers) Jen Selter (13.4M followers) Simeon Panda (8.1M followers) Top Instagram InfluencersJamie OliverDavid ChangJack Morris and Lauren BullenThe Bucket List FamilyChiara FerragniAlexa ChungJimmy ChinJannid Olsson DelérGrace BonneyHuda KattanZoe SuggSjana Elise EarpMassy Arias 1. Jamie Oliver Jamie Oliver, a world-renowned chef and restaurateur, is Instagram famous for his approachable and delicious-looking cuisine. His page reflects a mix of food pictures, recipes, and photos of his family and personal life. His love of beautiful food and teaching others to cook is clearly evident, which must be one of the many reasons why he has nearly seven million followers. 2. David Chang Celebrity chef David Chang is best known for his world-famous restaurants and big personality. Chang was a judge on Top Chef and created his own Netflix show called Ugly Delicious, both of which elevated his popularity and likely led to his huge followership on Instagram. Most of his feed is filled with food videos that will make you drool. View this post on Instagram 3. Jack Morris and Lauren Bullen Travel bloggers Jack Morris (@jackmorris) and Lauren Bullen (@gypsea_lust)have dream jobs -- the couple travels to some of the most beautiful places around the world and documents their trips on Instagram. They have developed a unique and recognizable Instagram aesthetic that their combined 4.8 million Instagram followers love, using the same few filters and posting the most striking travel destinations. View this post on Instagram 4. The Bucket List Family The Gee family, better known as the Bucket List Family, travel around the world with their three kids and post videos and images of their trips to YouTube and Instagram. They are constantly sharing pictures and stories of their adventures in exotic places. This nomad lifestyle is enjoyed by their 2.6 million followers. View this post on Instagram 5. Chiara Ferragni Chiara Ferragni is an Italian fashion influencer who started her blog The Blonde Salad to share tips, photos, and clothing lines. Ferragni has been recognized as one of the most influential people of her generation, listed on Forbes’ 30 Under 30 and the Bloglovin’ Award Blogger of the Year. 6. Alexa Chung Model and fashion designer Alexa Chung is Instagram famous for her elegant yet charming style and photos. After her modeling career, she collaborated with many brands like Mulberry and Madewell to create her own collection, making a name for herself in the fashion world. Today, she shares artistic yet fun photos with her 5.2 million Instagram followers. 7. Jimmy Chin Jimmy Chin is an award-winning professional photographer who captures high-intensity shots of climbing expeditions and natural panoramas. He has won multiple awards for his work, and his 3.2 million Instagram followers recognize him for his talent. 8. Jannid Olsson Delér Jannid Olsson Delér is a lifestyle and fashion blogger that gathered a huge social media following for her photos of outfits, vacations, and her overall aspirational life. Her 1.2 million followers look to her for travel and fashion inspirations. 9. Grace Bonney Design*Sponge is a design blog authored by Grace Bonney, an influencer recognized by the New York Times, Forbes, and other major publications for her impact on the creative community. Her Instagram posts reflect her elegant yet approachable creative advice, and nearly a million users follow her account for her bright and charismatic feed. 10. Huda Kattan Huda Kattan took the beauty world by storm -- her Instagram began with makeup tutorials and reviews and turned into a cosmetics empire. Huda now has 1.3 million Instagram followers and a company valued at $1.2 billion. Her homepage is filled with makeup videos and snaps of her luxury lifestyle. View this post on Instagram 11. Zoe Sugg Zoe Sugg runs a fashion, beauty, and lifestyle blog and has nearly 10 million followers on Instagram. She also has an incredibly successful YouTube channel and has written best-selling books on the experience of viral bloggers. Her feed consists mostly of food, her pug, selfies, and trendy outfits. View this post on Instagram 12. Sjana Elise Earp Sjana Elise Earp is a lifestyle influencer who keeps her Instagram feed full of beautiful photos of her travels. She actively promotes yoga and healthy living to her 1.4 million followers, becoming an advocate for an exercise program called SWEAT. 13. Massy Arias Personal trainer Massy Arias is known for her fitness videos and healthy lifestyle. Her feed aims to inspire her 2.6 million followers to keep training and never give up on their health. Arias has capitalized on fitness trends on Instagram and proven to both herself and her followers that exercise can improve all areas of your life. View this post on Instagram

                    24 Stunning Instagram Themes (& How to Borrow Them for Your Own Feed)
24 Stunning Instagram Themes (& How to Borrow Them for Your Own Feed)
Nowadays, Instagram is often someone's initial contact with a brand, and nearly half of its users shop on the platform each week. If it's the entryway for half of your potential sales, don't you want your profile to look clean and inviting? Taking the time to create an engaging Instagram feed aesthetic is one of the most effective ways to persuade someone to follow your business's Instagram account or peruse your posts. You only have one chance to make a good first impression — so it's critical that you put effort into your Instagram feed. Finding the perfect place to start is tough — where do you find inspiration? What color scheme should you use? How do you organize your posts so they look like a unit? We know you enjoy learning by example, so we've compiled the answers to all of these questions in a list of stunning Instagram themes. We hope these inspire your own feed's transformation. But beware, these feeds are so desirable, you'll have a hard time choosing just one. What is an Instagram theme?An instagram theme is a visual aesthetic created by individuals and brands to achieve a cohesive look on their Instagram feeds. Instagram themes help social media managers curate different types of content into a digital motif that brings a balanced feel to the profile. Tools to Create Your Own Instagram Theme Creating a theme on your own requires a keen eye for detail. When you’re editing several posts a week that follow the same theme, you’ll want to have a design tool handy to make that workflow easier. Pre-set filters, color palettes, and graphic elements are just a few of the features these tools use, but if you have a sophisticated theme to maintain, a few of these tools include advanced features like video editing and layout previews. Here are our top five favorite tools to use when editing photos for an Instagram theme. 1. VSCO Creators look to VSCO when they want to achieve the most unique photo edits. This app is one of the top-ranked photo editing tools among photographers because it includes advanced editing features without needing to pull out all the stops in Photoshop. If you’re in a hurry and want to create an Instagram theme quickly, use one of the 200+ VSCO presets including name-brand designs by Kodak, Agfa, and Ilford. If you’ll be including video as part of your content lineup on Instagram, you can use the same presets from the images so every square of content blends seamlessly into the next no matter what format it’s in. 2. FaceTune2 FaceTune2 is a powerful photo editing app that can be downloaded on the App Store or Google Play. The free version of the app includes all the basic editing features like brightness, lighting, cropping, and filters. The pro version gives you more detailed control over retouching and background editing. For video snippets, use FaceTune Video to make detailed adjustments right from your mobile device — you’ll just need to download the app separately for that capability. If you’re starting to test whether an Instagram theme is right for your brand, FaceTune2 is an affordable tool worth trying. 3. Canva You know Canva as a user-friendly and free option to create graphics, but it can be a powerful photo editing tool to curate your Instagram theme. For more abstract themes that mix imagery with graphic art, you can add shapes, textures, and text to your images. Using the photo editor, you can import your image and adjust the levels, add filters, and apply unique effects to give each piece of content a look that’s unique to your brand. 4. Adobe Illustrator Have you ever used Adobe Illustrator to create interesting overlays and tints for images? You can do the same thing to develop your Instagram theme. Traditionally, Adobe Illustrator is the go-to tool to create vectors and logos, but this software has some pretty handy features for creating photo filters and designs. Moreover, you can layout your artboards in an Instagram-style grid to see exactly how each image will appear in your feed. 5. Photoshop Photoshop is the most well-known photo editing software, and it works especially well for creating Instagram themes. If you have the capacity to pull out all the stops and tweak every detail, Photoshop will get the job done. Not only are the editing, filter, and adjustment options virtually limitless, Photoshop is great for batch processing the same edits across several images in a matter of seconds. You’ll also optimize your workflow by using photoshop to edit the composition, alter the background, and remove any unwanted components of an image without switching to another editing software to add your filter. With Photoshop, you have complete control over your theme which means you won’t have to worry about your profile looking exactly like someone else’s. Instagram ThemesTransitionBlack and WhiteBright ColorsMinimalistOne ColorTwo ColorsPastelsOne ThemePuzzleUnique AnglesText OnlyCheckerboardBlack or White BordersSame FilterFlatlaysVintageRepetitionMix-and-match Horizontal and Vertical BordersQuotesDark ColorsRainbowDoodleTextLinesAnglesHorizontal Lines 1. Transition If you aren’t set on one specific Instagram theme, consider the transition theme. With this aesthetic, you can experiment with merging colors every couple of images. For example, you could start with a black theme and include beige accents in every image. From there, gradually introduce the next color, in this case, blue. Eventually, you’ll find that your Instagram feed will seamlessly transition between the colors you choose which keeps things interesting without straying from a cohesive look and feel. 2. Black and White A polished black and white theme is a good choice to evoke a sense of sophistication. The lack of color draws you into the photo's main subject and suggests a timeless element to your business. @Lisedesmet's black and white feed, for instance, focuses the user’s gaze on the image's subject, like the black sneakers or white balloon. 3. Bright Colors If your company's brand is meant to imply playfulness or fun, there's probably no better way than to create a feed full of bright colors. Bright colors are attention-grabbing and lighthearted, which could be ideal for attracting a younger audience. @Aww.sam's feed, for instance, showcases someone who doesn't take herself too seriously. 4. Minimalist For an artsier edge, consider taking a minimalist approach to your feed, like @emwng does. The images are inviting and slightly whimsical in their simplicity, and cultivate feelings of serenity and stability. The pup pics only add wholesomeness to this minimalist theme. Plus, minimalist feeds are less distracting by nature, so it can be easier to get a true sense of the brand from the feed alone, without clicking on individual posts. 5. One Color One of the easiest ways to pick a theme for your feed is to choose one color and stick to it — this can help steer your creative direction, and looks clean and cohesive from afar. It's particularly appealing if you choose an aesthetically pleasing and calm color, like the soft pink used in the popular hashtag #blackwomeninpink. 6. Two Colors If you're interested in creating a highly cohesive feed but don't want to stick to the one-color theme, consider trying two. Two colors can help your feed look organized and clean — plus, if you choose branded colors, it can help you create cohesion between your other social media sites the website itself. I recommend choosing two contrasting colors for a punchy look like the one shown in @Dreaming_outloud’s profile. 7. Pastels Similar to the one-color idea, it might be useful to choose one color palette for your feed, like @creativekipi's use of pastels. Pastels, in particular, often used for Easter eggs or cupcake decorations, appear childlike and cheerful. Plus, they're captivating and unexpected. 8. One Subject As evident from @mustdoflorida's feed (and username), it's possible to focus your feed on one singular object or idea — like beach-related objects and activities in Florida. If you're aiming to showcase your creativity or photography skills, it could be compelling to create a feed where each post follows one theme. 9. Puzzle Creating a puzzle out of your feed is complicated and takes some planning, but can reap big rewards in terms of uniqueness and engaging an audience. @Juniperoats’ posts, for instance, make the most sense when you look at it from the feed, rather than individual posts. It's hard not to be both impressed and enthralled by the final result, and if you post puzzle piece pictures individually, you can evoke serious curiosity from your followers. 10. Unique Angles Displaying everyday items and activities from unexpected angles is sure to draw attention to your Instagram feed. Similar to the way lines create a theme, angles use direction to create interest. Taking an image of different subjects from similar angles can unite even the most uncommon photos into a consistent theme. 11. Text Only A picture is worth a thousand words, but how many pictures is a well-designed quote worth? Confident Woman Co. breaks the rules of Instagram that say images should have a face in them to get the best engagement. Not so with this Instagram theme. The bright colors and highlighted text make this layout aesthetically pleasing both in the Instagram grid format and as a one-off post on the feed. Even within this strict text-only theme, there’s still room to break up the monotony with a type-treated font and textured background like the last image does in the middle row. 12. Checkerboard If you're not a big fan of horizontal or vertical lines, you might try a checkerboard theme. Similar to horizontal lines, this theme allows you to alternate between content and images or colors as seen in @thefemalehustlers’ feed. 13. Black or White Borders While it is a bit jarring to have black or white borders outlining every image, it definitely sets your feed apart from everyone else's. @Beautifulandyummy, for instance, uses black borders to draw attention to her images, and the finished feed looks both polished and sophisticated. This theme will likely be more successful if you're aiming to sell fashion products or want to evoke an edgier feel for your brand. 14. Same Filter If you prefer uniformity, you'll probably like this Instagram theme, which focuses on using the same filter (or set of filters) for every post. From close up, this doesn't make much difference on your images, but from afar, it definitely makes the feed appear more cohesive. @marianna_hewitt, for example, is able to make her posts of hair, drinks, and fashion seem more refined and professional, simply by using the same filter for all her posts. 15. Flatlays If your primary goal with Instagram is to showcase your products, you might want a Flatlay theme. Flatlay is an effective way to tell a story simply by arranging objects in an image a certain way and makes it easier to direct viewers' attention to a product. As seen in @thedailyedited's feed, a flatlay theme looks fresh and modern. 16. Vintage If it aligns with your brand, vintage is a creative and striking aesthetic that looks both artsy and laid-back. And, while "vintage" might sound a little bit vague, it's easy to conjure. Simply try a filter like Slumber or Aden (built into Instagram), or play around with a third-party editing tool to find a soft, hazy filter that makes your photos look like they were taken from an old polaroid camera. 17. Repetition In @girleatworld's Instagram account, you can count on one thing to remain consistent throughout her feed: she's always holding up food in her hand. This type of repetition looks clean and engaging, and as a follower, it means I always recognize one of her posts as I'm scrolling through my own feed. Consider how you might evoke similar repetition in your own posts to create a brand image all your own. 18. Mix-and-match Horizontal and Vertical Borders While this admittedly requires some planning, the resulting feed is incredibly eye-catching and unique. Simply use the Preview app and choose two different white borders, Vela and Sole, to alternate between horizontal and vertical borders. The resulting feed will look spaced out and clean. 19. Quotes If you're a writer or content creator, you might consider creating an entire feed of quotes, like @thegoodquote feed, which showcases quotes on different mediums, ranging from paperback books to Tweets. Consider typing your quotes and changing up the color of the background, or handwriting your quotes and placing them near interesting objects like flowers or a coffee mug. 20. Dark Colors @JackHarding 's nature photos are nothing short of spectacular, and he highlights their beauty by filtering with a dark overtone. To do this, consider desaturating your content and using filters with cooler colors, like greens and blues, rather than warm ones. The resulting feed looks clean, sleek, and professional. 21. Rainbow One way to introduce color into your feed? Try creating a rainbow by slowly progressing your posts through the colors of the rainbow, starting at red and ending at purple (and then, starting all over again). The resulting feed is stunning. 22. Doodle Most people on Instagram stick to photos and filters, so to stand out, you might consider adding drawings or cartoon doodles on top of (or replacing) regular photo posts. This is a good idea if you're an artist or a web designer and want to draw attention to your artistic abilities — plus, it's sure to get a smile from your followers, like these adorable doodles shown below by @josie.doodles. 23. Content Elements Similar elements in your photos can create an enticing Instagram theme. In this example by The Container Store Custom Closets, the theme uses shelves or clothes in each image to visually bring the feed together. Rather than each photo appearing as a separate room, they all combine to create a smooth layout that displays The Container Store’s products in a way that feels natural to the viewer. 24. Structural Lines Something about this Instagram feed feels different, doesn’t it? Aside from the content focusing on skyscrapers, the lines of the buildings in each image turn this layout into a unique theme. If your brand isn’t in the business of building skyscrapers, you can still implement a theme like this by looking for straight or curved lines in the photos your capture. The key to creating crisp lines from the subjects in your photos is to snap them in great lighting and find symmetry in the image wherever possible. 25. Horizontal Lines If your brand does well with aligning photography with content, you might consider organizing your posts in a thoughtful way — for instance, creating either horizontal or vertical lines, with your rows alternating between colors, text, or even subject distance. @mariahb.makeup employs this tactic, and her feed looks clean and intriguing as a result. How to Create an Instagram Theme 1. Choose a consistent color palette. One major factor of any Instagram theme is consistency. For instance, you wouldn't want to regularly change your theme from black-and-white to rainbow — this could confuse your followers and damage your brand image. Of course, a complete company rebrand might require you to shift your Instagram strategy, but for the most part, you want to stay consistent with the types of visual content you post on Instagram. For this reason, you'll need to choose a color palette to adhere to when creating an Instagram theme. Perhaps you choose to use brand colors. LIKE.TG's Instagram, for instance, primarily uses blues, oranges, and teal, three colors prominently displayed on LIKE.TG's website and products. Alternatively, maybe you choose one of the themes listed above, such as black-and-white. Whatever the case, to create an Instagram theme, it's critical you stick to a few colors throughout all of your content. 2. Use the same filter for each post, or edit each post similarly. As noted above, consistency is a critical element in any Instagram theme, so you'll want to find your favorite one or two filters and use them for each of your posts. You can use Instagram's built-in filters, or try an editing app like VSCO or Snapseed. Alternatively, if you're going for a minimalist look, you might skip filters entirely and simply use a few editing features, like contrast and exposure. Whatever you choose, though, you'll want to continue to edit each of your posts similarly to create a cohesive feed. 3. Use a visual feed planner to plan posts far in advance. It's vital that you plan your Instagram posts ahead of time for a few different reasons, including ensuring you post a good variety of content and that you post it during a good time of day. Additionally, when creating an Instagram theme, you'll need to plan posts in advance to figure out how they fit together — like puzzle pieces, your individual pieces of content need to reinforce your theme as a whole. To plan posts far in advance and visualize how they reinforce your theme, you'll want to use a visual Instagram planner like Later or Planoly. Best of all, you can use these apps to preview your feed and ensure your theme is looking the way you want it to look before you press "Publish" on any of your posts. 4. Don't lock yourself into a theme you can't enjoy for the long haul. In middle school, I often liked to change my "look" — one day I aimed for preppy, and the next I chose a more athletic look. Of course, as I got older, I began to understand what style I could stick with for the long haul and started shopping for clothes that fit my authentic style so I wasn't constantly purchasing new clothes and getting sick of them a few weeks later. Similarly, you don't want to choose an Instagram theme you can't live with for a long time. Your Instagram theme should be an accurate reflection of your brand, and if it isn't, it probably won't last. Just because rainbow colors sound interesting at the get-go doesn't mean it's a good fit for your company's social media aesthetic as a whole. When in doubt, choose a more simple theme that provides you the opportunity to get creative and experiment without straying too far off-theme. How to Use an Instagram Theme on Your Profile 1. Choose what photos you want to post before choosing your theme. When you start an Instagram theme, there are so many options to choose from. Filters, colors, styles, angles — the choices are endless. But it’s important to keep in mind that these things won’t make your theme stand out. The content is still the star of the show. If the images aren’t balanced on the feed, your theme will look like a photo dump that happens to have the same filter on it. To curate the perfect Instagram theme, choose what photos you plan to post before choosing a theme. I highly recommend laying these photos out in a nine-square grid as well so you can see how the photos blend together. 2. Don’t forget the captions. Sure, no one is going to see the captions of your Instagram photos when they’re looking at your theme in the grid-view, but they will see them when you post each photo individually. There will be times when an image you post may be of something abstract, like the corner of a building, an empty suitcase, or a pair of sunglasses. On their own, these things might not be so interesting, but a thoughtful caption that ties the image to your overall theme can help keep your followers engaged when they might otherwise check out and keep scrolling past your profile. If you’re having a bit of writer’s block, check out these 201 Instagram captions for every type of post. 3. Switch up your theme with color blocks. Earlier, we talked about choosing a theme that you can commit to for the long haul. But there’s an exception to that rule — color transitions. Some of the best themes aren’t based on a specific color at all. Rather than using the same color palette throughout the Instagram feed, you can have colors blend into one another with each photo. This way, you can include a larger variety of photos without limiting yourself to specific hues. A Cohesive Instagram Theme At Your Fingertips Instagram marketing is more than numbers. As the most visual social media platform today, what you post and how it looks directly affects engagement, followers, and how your brand shows up online. A cohesive Instagram theme can help your brand convey a value proposition, promote a product, or execute a campaign. Colors and filters make beautiful themes, but there are several additional ways to stop your followers mid-scroll with a fun, unified aesthetic. Editor's note: This post was originally published in August 2018 and has been updated for comprehensiveness.
全球代理
 Why do SEO businesses need bulk IP addresses?
Why do SEO businesses need bulk IP addresses?
Search Engine Optimisation (SEO) has become an integral part of businesses competing on the internet. In order to achieve better rankings and visibility in search engine results, SEO professionals use various strategies and techniques to optimise websites. Among them, bulk IP addressing is an important part of the SEO business. In this article, we will delve into why SEO business needs bulk IP addresses and how to effectively utilise bulk IP addresses to boost your website's rankings and traffic.First, why does SEO business need bulk IP address?1. Avoid search engine blocking: In the process of SEO optimisation, frequent requests to search engines may be identified as malicious behaviour, resulting in IP addresses being blocked. Bulk IP addresses can be used to rotate requests to avoid being blocked by search engines and maintain the stability and continuity of SEO activities.2. Geo-targeting optimisation: Users in different regions may search through different search engines or search for different keywords. Bulk IP address can simulate different regions of the user visit, to help companies geo-targeted optimisation, to improve the website in a particular region of the search rankings.3. Multiple Keyword Ranking: A website is usually optimised for multiple keywords, each with a different level of competition. Batch IP address can be used to optimise multiple keywords at the same time and improve the ranking of the website on different keywords.4. Website content testing: Bulk IP address can be used to test the response of users in different regions to the website content, so as to optimise the website content and structure and improve the user experience.5. Data collection and competition analysis: SEO business requires a lot of data collection and competition analysis, and bulk IP address can help enterprises efficiently obtain data information of target websites.Second, how to effectively use bulk IP address for SEO optimisation?1. Choose a reliable proxy service provider: Choose a proxy service provider that provides stable and high-speed bulk IP addresses to ensure the smooth progress of SEO activities.2. Formulate a reasonable IP address rotation strategy: Formulate a reasonable IP address rotation strategy to avoid frequent requests to search engines and reduce the risk of being banned.3. Geo-targeted optimisation: According to the target market, choose the appropriate geographical location of the IP address for geo-targeted optimisation to improve the search ranking of the website in a particular region.4. Keyword Optimisation: Optimise the ranking of multiple keywords through bulk IP addresses to improve the search ranking of the website on different keywords.5. Content Optimisation: Using bulk IP addresses for website content testing, to understand the reaction of users in different regions, optimise website content and structure, and improve user experience.Third, application Scenarios of Bulk IP Address in SEO Business1. Data collection and competition analysis: SEO business requires a large amount of data collection and competition analysis, through bulk IP address, you can efficiently get the data information of the target website, and understand the competitors' strategies and ranking.2. Website Geo-targeting Optimisation: For websites that need to be optimised in different regions, bulk IP addresses can be used to simulate visits from users in different regions and improve the search rankings of websites in specific regions.3. Multi-keyword Ranking Optimisation: Bulk IP addresses can be used to optimise multiple keywords at the same time, improving the ranking of the website on different keywords.4. Content Testing and Optimisation: Bulk IP addresses can be used to test the response of users in different regions to the content of the website, optimise the content and structure of the website, and improve the user experience.Conclusion:In today's competitive Internet environment, SEO optimisation is a key strategy for companies to improve their website ranking and traffic. In order to achieve effective SEO optimisation, bulk IP addresses are an essential tool. By choosing a reliable proxy service provider, developing a reasonable IP address rotation strategy, geo-targeting optimisation and keyword optimisation, as well as conducting content testing and optimisation, businesses can make full use of bulk IP addresses to boost their website rankings and traffic, and thus occupy a more favourable position in the Internet competition.
1. Unlocking the Power of IP with Iproyal: A Comprehensive Guide2. Discovering the World of IP Intelligence with Iproyal3. Boosting Online Security with Iproyal's Cutting-Edge IP Solutions4. Understanding the Importance of IP Management: Exploring
1. Unlocking the Power of IP with Iproyal
A Comprehensive Guide2. Discovering the World of IP Intelligence with Iproyal3. Boosting Online Security with Iproyal's Cutting-Edge IP Solutions4. Understanding the Importance of IP Management
All You Need to Know About IPRoyal - A Reliable Proxy Service ProviderBenefits of Using IPRoyal:1. Enhanced Online Privacy:With IPRoyal, your online activities remain anonymous and protected. By routing your internet traffic through their secure servers, IPRoyal hides your IP address, making it virtually impossible for anyone to track your online behavior. This ensures that your personal information, such as banking details or browsing history, remains confidential.2. Access to Geo-Restricted Content:Many websites and online services restrict access based on your geographical location. IPRoyal helps you overcome these restrictions by providing proxy servers located in various countries. By connecting to the desired server, you can browse the internet as if you were physically present in that location, granting you access to region-specific content and services.3. Improved Browsing Speed:IPRoyal's dedicated servers are optimized for speed, ensuring a seamless browsing experience. By utilizing their proxy servers closer to your location, you can reduce latency and enjoy faster page loading times. This is particularly useful when accessing websites or streaming content that may be slow due to network congestion or geographical distance.Features of IPRoyal:1. Wide Range of Proxy Types:IPRoyal offers different types of proxies to cater to various requirements. Whether you need a datacenter proxy, residential proxy, or mobile proxy, they have you covered. Each type has its advantages, such as higher anonymity, rotational IPs, or compatibility with mobile devices. By selecting the appropriate proxy type, you can optimize your browsing experience.2. Global Proxy Network:With servers located in multiple countries, IPRoyal provides a global proxy network that allows you to choose the location that best suits your needs. Whether you want to access content specific to a particular country or conduct market research, their extensive network ensures reliable and efficient proxy connections.3. User-Friendly Dashboard:IPRoyal's intuitive dashboard makes managing and monitoring your proxy usage a breeze. From here, you can easily switch between different proxy types, select the desired server location, and view important usage statistics. The user-friendly interface ensures that even those with limited technical knowledge can make the most of IPRoyal's services.Conclusion:In a world where online privacy and freedom are increasingly threatened, IPRoyal provides a comprehensive solution to protect your anonymity and enhance your browsing experience. With its wide range of proxy types, global network, and user-friendly dashboard, IPRoyal is suitable for individuals, businesses, and organizations seeking reliable and efficient proxy services. Say goodbye to restrictions and safeguard your online presence with IPRoyal's secure and trusted proxy solutions.
1. Unveiling the World of Proxies: An In-Depth Dive into their Uses and Benefits2. Demystifying Proxies: How They Work and Why You Need Them3. The Power of Proxies: Unlocking a World of Online Possibilities4. Exploring the Role of Proxies in Data S
1. Unveiling the World of Proxies
An In-Depth Dive into their Uses and Benefits2. Demystifying Proxies
Title: Exploring the Role of Proxies in Ensuring Online Security and PrivacyDescription: In this blog post, we will delve into the world of proxies and their significance in ensuring online security and privacy. We will discuss the different types of proxies, their functionalities, and their role in safeguarding our online activities. Additionally, we will explore the benefits and drawbacks of using proxies, and provide recommendations for choosing the right proxy service.IntroductionIn today's digital age, where our lives have become increasingly interconnected through the internet, ensuring online security and privacy has become paramount. While we may take precautions such as using strong passwords and enabling two-factor authentication, another valuable tool in this endeavor is the use of proxies. Proxies play a crucial role in protecting our online activities by acting as intermediaries between our devices and the websites we visit. In this blog post, we will explore the concept of proxies, their functionalities, and how they contribute to enhancing online security and privacy.Understanding Proxies Proxies, in simple terms, are intermediate servers that act as connectors between a user's device and the internet. When we access a website through a proxy server, our request to view the webpage is first routed through the proxy server before reaching the website. This process helps ensure that our IP address, location, and other identifying information are not directly visible to the website we are accessing.Types of Proxies There are several types of proxies available, each with its own purpose and level of anonymity. Here are three common types of proxies:1. HTTP Proxies: These proxies are primarily used for accessing web content. They are easy to set up and can be used for basic online activities such as browsing, but they may not provide strong encryption or complete anonymity.2. SOCKS Proxies: SOCKS (Socket Secure) proxies operate at a lower level than HTTP proxies. They allow for a wider range of internet usage, including applications and protocols beyond just web browsing. SOCKS proxies are popular for activities such as torrenting and online gaming.Benefits and Drawbacks of Using Proxies Using proxies offers several advantages in terms of online security and privacy. Firstly, proxies can help mask our real IP address, making it difficult for websites to track our online activities. This added layer of anonymity can be particularly useful when accessing websites that may track or collect user data for advertising or other purposes.Moreover, proxies can also help bypass geolocation restrictions. By routing our internet connection through a proxy server in a different country, we can gain access to content that may be blocked or restricted in our actual location. This can be particularly useful for accessing streaming services or websites that are limited to specific regions.However, it is important to note that using proxies does have some drawbacks. One potential disadvantage is the reduced browsing speed that can occur when routing internet traffic through a proxy server. Since the proxy server acts as an intermediary, it can introduce additional latency, resulting in slower webpage loading times.Another potential concern with using proxies is the potential for malicious or untrustworthy proxy servers. If we choose a proxy service that is not reputable or secure, our online activities and data could be compromised. Therefore, it is crucial to research and select a reliable proxy service provider that prioritizes user security and privacy.Choosing the Right Proxy Service When selecting a proxy service, there are certain factors to consider. Firstly, it is essential to evaluate the level of security and encryption provided by the proxy service. Look for services that offer strong encryption protocols such as SSL/TLS to ensure that your online activities are protected.Additionally, consider the speed and availability of proxy servers. Opt for proxy service providers that have a wide network of servers in different locations to ensure optimal browsing speed and access to blocked content.Lastly, read user reviews and consider the reputation of the proxy service provider. Look for positive feedback regarding their customer support, reliability, and commitment to user privacy.Conclusion In an era where online security and privacy are of utmost importance, proxies offer a valuable tool for safeguarding our digital lives. By understanding the different types of proxies and their functionalities, we can make informed choices when it comes to selecting the right proxy service. While proxies provide enhanced privacy and security, it is crucial to be mindful of the potential drawbacks and choose reputable proxy service providers to ensure a safe online experience.
云服务
2018年,中小电商企业需要把握住这4个大数据趋势
2018年,中小电商企业需要把握住这4个大数据趋势
新的一年意味着你需要做出新的决定,这当然不仅限于发誓要减肥或者锻炼。商业和技术正飞速发展,你的公司需要及时跟上这些趋势。以下这几个数字能帮你在2018年制定工作规划时提供一定的方向。 人工智能(AI)在过去的12到18个月里一直是最热门的技术之一。11月,在CRM 软件服务提供商Salesforce的Dreamforce大会上,首席执行官Marc Benioff的一篇演讲中提到:Salesforce的人工智能产品Einstein每天都能在所有的云计算中做出了4.75亿次预测。 这个数字是相当惊人的。Einstein是在一年多前才宣布推出的,可现在它正在疯狂地“吐出”预测。而这仅仅是来自一个拥有15万客户的服务商。现在,所有主要的CRM服务商都有自己的人工智能项目,每天可能会产生超过10亿的预测来帮助公司改善客户交互。由于这一模式尚处于发展初期,所以现在是时候去了解能够如何利用这些平台来更有效地吸引客户和潜在客户了。 这一数字来自Facebook于2017年底的一项调查,该调查显示,人们之前往往是利用Messenger来与朋友和家人交流,但现在有越来越多人已经快速习惯于利用该工具与企业进行互动。 Facebook Messenger的战略合作伙伴关系团队成员Linda Lee表示,“人们提的问题有时会围绕特定的服务或产品,因为针对这些服务或产品,他们需要更多的细节或规格。此外,有时还会涉及到处理客户服务问题——或许他们已经购买了一个产品或服务,随后就会出现问题。” 当你看到一个3.3亿人口这个数字时,你必须要注意到这一趋势,因为在2018年这一趋势将很有可能会加速。 据Instagram在11月底发布的一份公告显示,该平台上80%的用户都关注了企业账号,每天有2亿Instagram用户都会访问企业的主页。与此相关的是,Instagram上的企业账号数量已经从7月的1500万增加到了2500万。 根据该公司的数据显示,Instagram上三分之一的小企业表示,他们已经通过该平台建立起了自己的业务;有45%的人称他们的销售额增加了;44%的人表示,该平台帮助了他们在其他城市、州或国家销售产品。 随着视频和图片正在吸引越多人们的注意力,像Instagram这样的网站,对B2C和B2B公司的重要性正在与日俱增。利用Instagram的广泛影响力,小型企业可以用更有意义的方式与客户或潜在客户进行互动。 谈到亚马逊,我们可以列出很多吸引眼球的数字,比如自2011年以来,它向小企业提供了10亿美元的贷款。而且在2017年的网络星期一,亚马逊的当天交易额为65.9亿美元,成为了美国有史以来最大的电商销售日。同时,网络星期一也是亚马逊平台卖家的最大销售日,来自全世界各地的顾客共从这些小企业订购了近1.4亿件商品。 亚马逊表示,通过亚马逊app订购的手机用户数量增长了50%。这也意味着,有相当数量的产品是通过移动设备销售出的。 所有这些大数据都表明,客户与企业的互动在未来将会发生巨大的变化。有些发展会比其他的发展更深入,但这些数字都说明了该领域的变化之快,以及技术的加速普及是如何推动所有这些发展的。 最后,希望这些大数据可以对你的2018年规划有一定的帮助。 (编译/LIKE.TG 康杰炜)
2020 AWS技术峰会和合作伙伴峰会线上举行
2020 AWS技术峰会和合作伙伴峰会线上举行
2020年9月10日至11日,作为一年一度云计算领域的大型科技盛会,2020 AWS技术峰会(https://www.awssummit.cn/) 正式在线上举行。今年的峰会以“构建 超乎所见”为主题,除了展示AWS最新的云服务,探讨前沿云端技术及企业最佳实践外,还重点聚焦垂直行业的数字化转型和创新。AWS宣布一方面加大自身在垂直行业的人力和资源投入,组建行业团队,充分利用AWS的整体优势,以更好的发掘、定义、设计、架构和实施针对垂直行业客户的技术解决方案和场景应用;同时携手百家中国APN合作伙伴发布联合解决方案,重点覆盖金融、制造、汽车、零售与电商、医疗与生命科学、媒体、教育、游戏、能源与电力九大行业,帮助这些行业的客户实现数字化转型,进行数字化创新。峰会期间,亚马逊云服务(AWS)还宣布与毕马威KPMG、神州数码分别签署战略合作关系,推动企业上云和拥抱数字化。 亚马逊全球副总裁、AWS大中华区执董事张文翊表示,“AWS一直致力于不断借助全球领先的云技术、广泛而深入的云服务、成熟和丰富的商业实践、全球的基础设施覆盖,安全的强大保障以及充满活力的合作伙伴网络,加大在中国的投入,助力中国客户的业务创新、行业转型和产业升级。在数字化转型和数字创新成为‘新常态’的今天,我们希望通过AWS技术峰会带给大家行业的最新动态、全球前沿的云计算技术、鲜活的数字创新实践和颇具启发性的文化及管理理念,推动中国企业和机构的数字化转型和创新更上层楼。” 构建场景应用解决方案,赋能合作伙伴和客户 当前,传统企业需要上云,在云上构建更敏捷、更弹性和更安全的企业IT系统,实现数字化转型。同时,在实现上云之后,企业又迫切需要利用现代应用开发、大数据、人工智能与机器学习、容器技术等先进的云技术,解决不断涌现的业务问题,实现数字化创新,推动业务增长。 亚马逊云服务(AWS)大中华区专业服务总经理王承华表示,为了更好的提升行业客户体验,截至目前,AWS在中国已经发展出了数十种行业应用场景及相关的技术解决方案。 以中国区域部署的数字资产管理和云上会议系统两个应用场景解决方案为例。其中,数字资产盘活机器人让客户利用AWS云上资源低成本、批处理的方式标记数字资产,已经在银行、证券、保险领域率先得到客户青睐;AWS上的BigBlueButton,让教育机构或服务商可以在AWS建一套自己的在线会议系统,尤其适合当前急剧增长的在线教育需求。 这些行业应用场景解决方案经过客户验证成熟之后,AWS把它们转化为行业解决方案,赋能APN合作伙伴,拓展给更多的行业用户部署使用。 发布百家APN合作伙伴联合解决方案 打造合作伙伴社区是AWS服务企业客户的一大重点,也是本次峰会的亮点。AWS通过名为APN(AWS合作伙伴网络)的全球合作伙伴计划,面向那些利用AWS为客户构建解决方案的技术和咨询企业,提供业务支持、技术支持和营销支持,从而赋能这些APN合作伙伴,更好地满足各行各业、各种规模客户地需求。 在于9月9日举行的2020 AWS合作伙伴峰会上,AWS中国区生态系统及合作伙伴部总经理汪湧表示,AWS在中国主要从四个方面推进合作伙伴网络的构建。一是加快AWS云服务和功能落地,从而使合作伙伴可以利用到AWS全球最新的云技术和服务来更好地服务客户;二是推动跨区域业务扩展,帮助合作伙伴业务出海,也帮助全球ISV落地中国,同时和区域合作伙伴一起更好地服务国内各区域市场的客户;三是与合作伙伴一起着力传统企业上云迁移;四是打造垂直行业解决方案。 一直以来,AWS努力推动将那些驱动中国云计算市场未来、需求最大的云服务优先落地中国区域。今年上半年,在AWS中国区域已经落地了150多项新服务和功能,接近去年的全年总和。今年4月在中国落地的机器学习服务Amazon SageMaker目前已经被德勤、中科创达、东软、伊克罗德、成都潜在(行者AI)、德比软件等APN合作伙伴和客户广泛采用,用以创新以满足层出不穷的业务需求,推动增长。 联合百家APN合作伙伴解决方案打造垂直行业解决方案是AWS中国区生态系统构建的战略重点。 以汽车行业为例,东软集团基于AWS构建了云原生的汽车在线导航业务(NOS),依托AWS全球覆盖的基础设施、丰富的安全措施和稳定可靠的云平台,实现车规级的可靠性、应用程序的持续迭代、地图数据及路况信息的实时更新,服务中国车企的出海需求。 上海速石科技公司构建了基于AWS云上资源和用户本地算力的一站式交付平台,为那些需要高性能计算、海量算力的客户,提供一站式算力运营解决方案,目标客户涵盖半导体、药物研发、基因分析等领域。利用云上海量的算力,其客户在业务峰值时任务不用排队,极大地提高工作效率,加速业务创新。 外研在线在AWS上构建了Unipus智慧教学解决方案,已经服务于全国1700多家高校、1450万师生。通过将应用部署在AWS,实现SaaS化的交付模式,外研在线搭建了微服务化、自动伸缩的架构,可以自动适应教学应用的波峰波谷,提供稳定、流畅的体验,并且节省成本。 与毕马威KPMG、神州数码签署战略合作 在2020AWS技术峰会和合作伙伴峰会上,AWS还宣布与毕马威、神州数码签署战略合作关系,深化和升级合作。 AWS与毕马威将在中国开展机器学习、人工智能和大数据等领域的深入合作,毕马威将基于AWS云服务,结合其智慧之光系列数字化解决方案,为金融服务、制造业、零售、快消、以及医疗保健和生命科学等行业客户,提供战略规划、风险管理、监管与合规等咨询及实施服务。AWS将与神州数码将在赋能合作伙伴上云转型、全生命周期管理及助力全球独立软件开发商(ISV)落地中国方面展开深入合作,助力中国企业和机构的数字化转型与创新。
2021re:Invent全球大会圆满落幕 亚马逊云科技致敬云计算探路者
2021re
Invent全球大会圆满落幕 亚马逊云科技致敬云计算探路者
本文来源:LIKE.TG 作者:Ralf 全球最重磅的云计算大会,2021亚马逊云科技re:Invent全球大会已圆满落幕。re:Invent大会是亚马逊云科技全面展示新技术、产品、功能和服务的顶级行业会议,今年更是迎来十周年这一里程碑时刻。re:Invent,中文意为重塑,是亚马逊云科技一直以来坚持的“精神内核”。 作为Andy Jassy和新CEO Adam Selipsky 交接后的第一次re:Invent大会,亚马逊云科技用诸多新服务和新功能旗帜鲜明地致敬云计算探路者。 致敬云计算探路者 亚马逊云科技CEO Adam Selipsky盛赞云上先锋客户为“探路者”,他说,“这些客户都有巨大的勇气和魄力通过上云做出改变。他们勇于探索新业务、新模式,积极重塑自己和所在的行业。他们敢于突破边界,探索未知领域。有时候,我们跟客户共同努力推动的这些工作很艰难,但我们喜欢挑战。我们把挑战看作探索未知、发现新机遇的机会。回过头看,每一个这样的机构都是在寻找一条全新的道路。他们是探路者。” Adam 认为,探路者具有三个特征:创新不息,精进不止(Constant pursuit of a better way);独识卓见,领势而行(Ability to see what others don’t);授人以渔,赋能拓新(Enable others to forge their own paths)。 十五年前,亚马逊云科技缔造了云计算概念,彼时IT和基础设施有很大的局限。不仅贵,还反应慢、不灵活,大大限制了企业的创新。亚马逊云科技意识到必须探索一条新的道路,重塑企业IT。 从2006年的Amazon S3开始,IT应用的基础服务,存储、计算、数据库不断丰富。亚马逊云科技走过的15年历程 也是云计算产业发展的缩影。 目前,S3现在存储了超过100万亿个对象,EC2每天启用超过6000万个新实例。包括S3和EC2,亚马逊云科技已经提供了200大类服务,覆盖了计算、存储、网络、安全、数据库、数据分析、人工智能、物联网、混合云等各个领域,甚至包括最前沿的量子计算服务和卫星数据服务 (图:亚马逊全球副总裁、亚马逊云科技大中华区执行董事张文翊) 对于本次大会贯穿始终的探路者主题,亚马逊全球副总裁、亚马逊云科技大中华区执行董事张文翊表示:“大家对这个概念并不陌生,他们不被规则所限,从不安于现状;他们深入洞察,开放视野;还有一类探路者,他们不断赋能他人。我们周围有很多鲜活的例子,无论是科研人员发现新的治疗方案挽救生命,还是为身处黑暗的人带去光明; 无论是寻找新的手段打破物理边界,还是通过云进行独特的创新,探路源源不断。” 技术升级创新不断 本次re:Invent大会,亚马逊云科技发布涵盖计算、物联网、5G、无服务器数据分析、大机迁移、机器学习等方向的多项新服务和功能,为业界带来大量重磅创新服务和产品技术更新,包括发布基于新一代自研芯片Amazon Graviton3的计算实例、帮助大机客户向云迁移的Amazon Mainframe Modernization、帮助企业构建移动专网的Amazon Private 5G、四个亚马逊云科技分析服务套件的无服务器和按需选项以及为垂直行业构建的云服务和解决方案,如构建数字孪生的服务Amazon IoT TwinMaker和帮助汽车厂商构建车联网平台的Amazon IoT FleetWise。 (图:亚马逊云科技大中华区产品部总经理顾凡) 亚马逊云科技大中华区产品部总经理顾凡表示,新一代的自研ARM芯片Graviton3性能有显著提升。针对通用的工作负载,Graviton3比Graviton2的性能提升25%,而专门针对高性能计算里的科学类计算,以及机器学习等这样的负载会做更极致的优化。针对科学类的计算负载,Graviton3的浮点运算性能比Graviton2提升高达2倍;像加密相关的工作负载产生密钥加密、解密,这部分性能比Graviton2会提升2倍,针对机器学习负载可以提升高达3倍。Graviton3实例可以减少多达60%的能源消耗。 新推出的Amazon Private 5G,让企业可以轻松部署和扩展5G专网,按需配置。Amazon Private 5G将企业搭建5G专网的时间从数月降低到几天。客户只需在亚马逊云科技的控制台点击几下,就可以指定想要建立移动专网的位置,以及终端设备所需的网络容量。亚马逊云科技负责交付、维护、建立5G专网和连接终端设备所需的小型基站、服务器、5G核心和无线接入网络(RAN)软件,以及用户身份模块(SIM卡)。Amazon Private 5G可以自动设置和部署网络,并按需根据额外设备和网络流量的增长扩容。 传统工业云化加速 在亚马逊云科技一系列新服务和新功能中,针对传统工业的Amazon IoT TwinMaker和Amazon IoT FleetWise格外引人关注。 就在re:Invent大会前一天。工业和信息化部发布《“十四五”信息化和工业化深度融合发展规划》(《规划》),《规划》明确了到2025年发展的分项目标,其中包括工业互联网平台普及率达45%。 亚马逊云科技布局物联网已经有相当长的时间。包括工业互联网里的绿色产线的维护、产线的质量监控等,在数字孪生完全构建之前,已经逐步在实现应用的实体里面。亚马逊云科技大中华区产品部计算与存储总监周舸表示,“在产线上怎么自动化地去发现良品率的变化,包括Amazon Monitron在产线里面可以直接去用,这些传感器可以监测震动、温度等,通过自动的建模去提早的预测可能会出现的问题,就不用等到灾难发生,而是可以提早去换部件或者加点机油解决潜在问题。” 周舸认为工业互联的场景在加速。但很多中小型的工厂缺乏技术能力。“Amazon IoT TwinMaker做数字孪生的核心,就是让那些没有那么强的能力自己去构建或者去雇佣非常专业的构建的公司,帮他们搭建数字孪生,这个趋势是很明确的,我们也在往这个方向努力。” 对于汽车工业,特别是新能源汽车制造。数据的收集管理已经变得越来越重要。Amazon IoT FleetWise,让汽车制造商更轻松、经济地收集、管理车辆数据,同时几乎实时上传到云端。通过Amazon IoT FleetWise,汽车制造商可以轻松地收集和管理汽车中任何格式的数据(无论品牌、车型或配置),并将数据格式标准化,方便在云上轻松进行数据分析。Amazon IoT FleetWise的智能过滤功能,帮助汽车制造商近乎实时地将数据高效上传到云端,为减少网络流量的使用,该功能也允许开发人员选择需要上传的数据,还可以根据天气条件、位置或汽车类型等参数来制定上传数据的时间规则。当数据进入云端后,汽车制造商就可以将数据应用于车辆的远程诊断程序,分析车队的健康状况,帮助汽车制造商预防潜在的召回或安全问题,或通过数据分析和机器学习来改进自动驾驶和高级辅助驾驶等技术。
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1210保税备货模式是什么?1210跨境电商中找到适合的第三方支付接口平台
1210保税备货模式是什么?1210跨境电商中找到适合的第三方支付接口平台
  1210保税备货模式是一种跨境电商模式,它允许电商平台在境外仓库存储商品,以便更快、更便宜地满足国内消费者的需求。这种模式的名称“1210”代表了其核心特点,即1天出货、2周入仓、10天达到终端用户。它是中国跨境电商行业中的一种创新模式,为消费者提供了更快速、更便宜的购物体验,同时也促进了国际贸易的发展。   在1210保税备货模式中,电商平台会在国外建立仓库,将商品直接从生产国或供应商处运送到境外仓库进行存储。   由于商品已经在国内仓库存储,当消费者下单时,可以更快速地发货,常常在1天内出货,大大缩短了交付时间。   1210模式中,商品已经进入国内仓库,不再需要跨越国际海运、海关清关等环节,因此物流成本较低。   由于商品直接从生产国或供应商处运送到境外仓库,不需要在国内仓库大量储备库存,因此降低了库存成本。   1210模式可以更精确地控制库存,减少滞销和过期商品,提高了库存周转率。   在实施1210保税备货模式时,选择合适的第三方支付接口平台也是非常重要的,因为支付环节是电商交易中不可或缺的一环。   确保第三方支付接口平台支持国际信用卡支付、外币结算等功能,以便国际消费者能够顺利完成支付。   提供多种支付方式,以满足不同消费者的支付习惯。   第三方支付接口平台必须具备高度的安全性,包含数据加密、反欺诈措施等,以保护消费者的支付信息和资金安全。   了解第三方支付接口平台的跨境结算机制,确保可以顺利将国际销售收入转换为本地货币,并减少汇率风险。   选择一个提供良好技术支持和客户服务的支付接口平台,以应对可能出现的支付问题和故障。   了解第三方支付接口平台的费用结构,包含交易费率、结算费用等,并与自身业务规模和盈利能力相匹配。   确保第三方支付接口平台可以与电商平台进行顺畅的集成,以实现订单管理、库存控制和财务管理的无缝对接。   考虑未来业务扩展的可能性,选择一个具有良好扩展性的支付接口平台,以适应不断增长的交易量和新的市场需求。   在选择适合的第三方支付接口平台时,需要考虑到以上支付功能、安全性、成本、技术支持等因素,并与自身业务需求相匹配。 本文转载自:https://www.ipaylinks.com/
2023年德国VAT注册教程有吗?增值税注册注意的事及建议
2023年德国VAT注册教程有吗?增值税注册注意的事及建议
  作为欧洲的经济大国,德国吸引了许多企业在该地区抢占市场。在德国的商务活动涉及增值税(VAT)难题是在所难免的。   1、决定是否务必注册VAT   2023年,德国的增值税注册门槛是前一年销售额超过17500欧。对在德国有固定经营场所的外国企业,不管销售状况怎样,都应开展增值税注册。   2、备好所需的材料   企业注册证实   业务地址及联络信息   德国银行帐户信息   预估销售信息   公司官方文件(依据公司类型可能有所不同)   3、填写申请表   要访问德国税务局的官网,下载并递交增值税注册申请表。确保填好精确的信息,由于不准确的信息可能会致使申请被拒或审计耽误。   4、提交申请   填写申请表后,可以经过电子邮箱把它发给德国税务局,或在某些地区,可以网上申请申请。确保另附全部必须的文件和信息。   5、等待审批   递交了申请,要耐心地等待德国税务局的准许。因为税务局的工作负荷和个人情况,准许时长可能会有所不同。一般,审计可能需要几周乃至几个月。   6、得到VAT号   假如申请获得批准,德国税务局可能授于一个增值税号。这个号码应当是德国增值税申报和支付业务视频的关键标示。   7、逐渐申报和付款   获得了增值税号,你应该根据德国的税收要求逐渐申报和付款。根据规定时间表,递交增值税申请表并缴纳相应的税款。   注意的事和提议   填写申请表时,确保信息精确,避免因错误报告导致审批耽误。   假如不强化对德国税制改革的探索,提议寻求专业税务顾问的支持,以保障申请和后续申报合规。   储存全部申请及有关文件的副本,用以日后的审查和审计。 本文转载自:https://www.ipaylinks.com/
2023年注册代理英国VAT的费用
2023年注册代理英国VAT的费用
  在国际贸易和跨境电商领域,注册代理英国增值税(VAT)是一项关键且必要的步骤。2023年,许多企业为了遵守英国的税务法规和合规要求,选择注册代理VAT。   1. 注册代理英国VAT的背景:   英国是一个重要的国际贸易和电商市场,许多企业选择在英国注册VAT,以便更好地服务英国客户,并利用英国的市场机会。代理VAT是指经过一个英国境内的注册代理公司进行VAT申报和纳税,以简化税务流程。   2. 费用因素:   注册代理英国VAT的费用取决于多个因素,包括但不限于:   业务规模: 企业的业务规模和销售额可能会影响注册代理VAT的费用。常常来说,销售额较大的企业可能需要支付更高的费用。   代理公司选择: 不同的注册代理公司可能收取不同的费用。选择合适的代理公司很重要,他们的费用结构可能会因公司而异。   服务范围: 代理公司可能提供不同的服务范围,包括申报、纳税、咨询等。你选择的服务范围可能会影响费用。   附加服务: 一些代理公司可能提供附加服务,如法律咨询、报告生成等,这些服务可能会增加费用。   复杂性: 如果的业务涉及复杂的税务情况或特殊需求,可能需要额外的费用。   3. 典型费用范围:   2023年注册代理英国VAT的费用范围因情况而异,但常常可以在几百英镑到数千英镑之间。对小规模企业,费用可能较低,而对大规模企业,费用可能较高。   4. 寻求报价:   如果计划在2023年注册代理英国VAT,建议与多家注册代理公司联系,获得费用报价。这样可以比较不同公司的费用和提供的服务,选择最适合你需求的代理公司。   5. 其他费用考虑:   除了注册代理VAT的费用,你还应考虑其他可能的费用,如VAT申报期限逾期罚款、税务咨询费用等。保持合规和及时申报可以避免这些额外费用。   6. 合理预算:   在注册代理英国VAT时,制定合理的预算非常重要。考虑到不同因素可能会影响费用,确保有足够的资金来支付这些费用是必要的。   2023年注册代理英国VAT的费用因多个因素而异。了解这些因素,与多家代理公司沟通,获取费用报价,制定合理的预算,会有助于在注册VAT时做出聪明的决策。确保业务合规,并寻求专业税务顾问的建议,以保障一切顺利进行。 本文转载自:https://www.ipaylinks.com/
广告投放
2021年B2B外贸跨境获客催化剂-行业案例之测控
2021年B2B外贸跨境获客催化剂-行业案例之测控
随着时间的推移,数字化已经在中国大量普及,越来越多的B2B企业意识到数字营销、内容营销、社交传播可以帮助业务加速推进。但是在和大量B2B出海企业的合作过程中,我们分析发现在实际的营销中存在诸多的瓶颈和痛点。 例如:传统B2B营销方式获客难度不断增大、获客受众局限、询盘成本高但质量不高、询盘数量增长不明显、线下展会覆盖客户的流失等,这些都是每天考验着B2B营销人的难题。 说到这些痛点和瓶颈,就不得不提到谷歌广告了,对比其他推广平台,Google是全球第一大搜索引擎,全球月活跃用户高达50亿人,覆盖80%全球互联网用户。受众覆盖足够的前提下,谷歌广告( Google Ads)还包括多种广告形式:搜索广告、展示广告(再营销展示广告、竞对广告)、视频广告、发现广告等全方位投放广告,关键字精准定位投放国家的相关客户,紧跟采购商的采购途径,增加获客。可以完美解决上面提到的痛点及瓶颈。 Google 360度获取优质流量: Google线上营销产品全方位助力: 营销网站+黄金账户诊断报告+定期报告=效果。 Google Ads为太多B2B出海企业带来了红利,这些红利也并不是简简单单就得来的,秘诀就是贵在坚持。多年推广经验总结:即使再好的平台,也有部分企业运营效果不好的时候,那应该怎么办?像正处在这种情况下的企业就应该放弃吗? 答案是:不,我们应该继续优化,那为什么这么说呢?就是最近遇到一个很典型的案例一家测控行业的企业,仅仅投放2个月的Google Ads,就因为询盘数量不多(日均150元,3-4封/月),投资回报率不成正比就打算放弃。 但其实2个月不足以说明什么,首先谷歌推广的探索期就是3个月,2个月基本处于平衡稳定的阶段。 其次对于刚刚做谷歌广告的新公司来说,国外客户是陌生的,即使看到广告进到网站也并不会第一时间就留言,货比三家,也会增加采购商的考虑时间,一直曝光在他的搜索结果页产生熟悉度,总会增加一些决定因素。 再有日预算150元,不足以支撑24小时点击,有时在搜索量较大的时候却没有了预算,导致了客户的流失。 最后不同的行业账户推广形式及效果也不一样,即使行业一样但是网站、公司实力等因素就不可能一模一样,即使一模一样也会因为流量竞争、推广时长等诸多因素导致效果不一样。 成功都是摸索尝试出来的,这个企业账户也一样,经过我们进一步的沟通分析决定再尝试一次, 这一次深度的分析及账户的优化后,最终效果翻了2-3倍,做到了从之前的高成本、低询盘量到现在低成本、高询盘的过渡。 这样的一个操作就是很好地开发了这个平台,通过充分利用达到了企业想要的一个效果。所以说啊,当谷歌广告做的不好的时候不应该放弃,那我们就来一起看一下这个企业是如何做到的。 2021年B2B外贸跨境获客催化剂-行业案例之测控(上) 一、主角篇-雷达液位测量仪 成立时间:2010年; 业务:微波原理的物料雷达液位测量与控制仪器生产、技术研发,雷达开发; 产业规模:客户分布在11个国家和地区,包括中国、巴西、马来西亚和沙特阿拉伯; 公司推广目标:低成本获得询盘,≤200元/封。 本次分享的主角是测控行业-雷达液位测量仪,目前预算250元/天,每周6-7封有效询盘,广告形式以:搜索广告+展示再营销为主。 过程中从一开始的控制预算150/天以搜索和展示再营销推广形式为主,1-2封询盘/周,询盘成本有时高达1000/封,客户预期是100-300的单个询盘成本,对于公司来说是能承受的价格。 以增加询盘数量为目的尝试过竞对广告和Gmail广告的推广,但投放过程中的转化不是很明显,一周的转化数据只有1-2个相比搜索广告1:5,每天都会花费,因为预算问题客户计划把重心及预算放在搜索广告上面,分析后更改账户广告结构还是以搜索+再营销为主,所以暂停这2种广告的推广。 账户调整后大约2周数据表现流量稳定,每周的点击、花费及转化基本稳定,平均为588:1213:24,询盘提升到了3-5封/周。 账户稳定后新流量的获取方法是现阶段的目标,YouTube视频广告,几万次的展示曝光几天就可以完成、单次观看价格只有几毛钱,传达给客户信息建议后,达成一致,因为这正是该客户一直所需要的低成本获取流量的途径; 另一个计划投放视频广告的原因是意识到想要增加网站访客进而增加获客只靠文字和图片已经没有太多的竞争力了,同时换位思考能够观看到视频也能提升采购商的购买几率。 所以就有了这样的后期的投放规划:搜索+展示再营销+视频广告300/天的推广形式,在谷歌浏览器的搜索端、B2B平台端、视频端都覆盖广告,实现尽可能多的客户数量。 关于具体的关于YouTube视频广告的介绍我也在另一篇案例里面有详细说明哦,指路《YouTube视频广告助力B2B突破瓶颈降低营销成本》,邀请大家去看看,干货满满,绝对让你不虚此行~ 二、方向转变篇-推广产品及国家重新定位 下面我就做一个账户实际转变前后的对比,这样大家能够更清楚一些: 最关键的来了,相信大家都想知道这个转变是怎么来的以及谷歌账户做了哪些调整把效果做上来的。抓住下面几点,相信你也会有所收获: 1. 产品投放新定位 因为企业是专门研发商用雷达,所以只投放这类的测量仪,其中大类主要分为各种物料、料位、液位测量仪器,其他的不做。根据关键字规划师查询的产品关键字在全球的搜索热度,一开始推广的只有雷达液位计/液位传感器/液位测量作为主推、无线液位变送器作为次推,产品及图片比较单一没有太多的竞争力。 后期根据全球商机洞察的行业产品搜索趋势、公司计划等结合统计结果又添加了超声波传感器、射频/电容/导纳、无线、制导雷达液位传感器、高频雷达液位变送器、无接触雷达液位计,同时增加了图片及详情的丰富性,做到了行业产品推广所需的多样性丰富性。像静压液位变送器、差压变送器没有他足够的搜索热度就没有推广。 2. 国家再筛选 转变前期的国家选取是根据海关编码查询的进口一直处在增长阶段的国家,也参考了谷歌趋势的国家参考。2018年全球进口(采购量)200.58亿美金。 采购国家排名:美国、德国、日本、英国、法国、韩国、加拿大、墨西哥、瑞典、荷兰、沙特阿拉伯。这些国家只能是参考切记跟风投放,疫情期间,实际的询盘国家还要靠数据和时间积累,做到及时止损即可。 投放过程不断摸索,经过推广数据总结,也根据实际询盘客户所在地暂停了部分国家,例如以色列、日本、老挝、摩纳哥、卡塔尔等国家和地区,加大力度投放巴西、秘鲁、智利、俄罗斯等国家即提高10%-20%的出价,主要推广地区还是在亚洲、南美、拉丁美洲、欧洲等地。 发达国家像英美加、墨西哥由于采购商的参考层面不同就单独拿出来给一小部分预算,让整体的预算花到发展中国家。通过后期每周的询盘反馈及时调整国家出价,有了现在的转变: 转变前的TOP10消耗国家: 转变后的TOP10消耗国家: 推广的产品及国家定下来之后,接下来就是做账户了,让我们继续往下看。 三、装备篇-账户投放策略 说到账户投放,前提是明确账户投放策略的宗旨:确保投资回报率。那影响投资回报率的效果指标有哪些呢?其中包含账户结构 、效果再提升(再营销、视频、智能优化等等)、网站着陆页。 那首先说明一下第一点:账户的结构,那账户结构怎么搭建呢?在以产品营销全球为目标的广告投放过程中,该客户在3个方面都有设置:预算、投放策略、搜索+再营销展示广告组合拳,缺一不可,也是上面转变后整体推广的总结。 账户结构:即推广的广告类型主要是搜索广告+再营销展示广告,如下图所示,下面来分别说明一下。 1、搜索广告结构: 1)广告系列 创建的重要性:我相信有很大一部分企业小伙伴在创建广告系列的时候都在考虑一个大方向上的问题:广告系列是针对所有国家投放吗?还是说不同的广告系列投放不同的国家呢? 实操规则:其实建议选择不同广告系列投放不同的国家,为什么呢?因为每个国家和每个国家的特点不一样,所以说在广告投放的时候应该区分开,就是着重性的投放。所以搜索广告系列的结构就是区分开国家,按照大洲划分(投放的国家比较多的情况下,这样分配可以观察不同大洲的推广数据以及方便对市场的考察)。 优化技巧:这样操作也方便按照不同大洲的上班时间调整广告投放时间,做到精准投放。 数据分析:在数据分析方面更方便观察不同大洲的数据效果,从而调整国家及其出价;进而能了解到不同大洲对于不同产品的不同需求,从而方便调整关键字。 这也引出了第二个重点调整对象—关键字,那关键字的选取是怎么去选择呢? 2)关键字 分为2部分品牌词+产品关键字,匹配形式可以采用广泛带+修饰符/词组/完全。 精准投放关键字: 品牌词:品牌词是一直推广的关键字,拓展品牌在海外的知名度应为企业首要的目的。 广告关键词:根据投放1个月数据发现:该行业里有一部分是大流量词(如Sensors、water level controller、Ultrasonic Sensor、meter、transmitter),即使是关键字做了完全匹配流量依然很大,但是实际带来的转化却很少也没有带来更多的询盘,这些词的调整过程是从修改匹配形式到降低出价再到暂停,这种就属于无效关键字了,我们要做到的是让预算花费到具体的产品关键字上。 其次流量比较大的词(如+ultrasound +sensor)修改成了词组匹配。还有一类词虽然搜索量不大但是有效性(转化次数/率)较高(例如:SENSOR DE NIVEL、level sensor、capacitive level sensor、level sensor fuel),针对这些关键字再去投放的时候出价可以相对高一些,1-3元即可。调整后的关键字花费前后对比,整体上有了大幅度的变化: 转变前的TOP10热力关键字: 转变后的TOP10热力关键字: PS: 关键字状态显示“有效”—可以采用第一种(防止错失账户投放关键字以外其他的也适合推广的该产品关键字)、如果投放一周后有花费失衡的状态可以把该关键字修改为词组匹配,观察一周还是失衡状态可改为完全匹配。 关键字状态显示“搜索量较低”—广泛匹配观察一个月,如果依然没有展示,建议暂停,否则会影响账户评级。 3)调整关键字出价 次推产品的出价都降低到了1-2元,主推产品也和实际咨询、平均每次点击费用做了对比调整到了3-4元左右(这些都是在之前高出价稳定排名基础后调整的)。 4)广告系列出价策略 基本包含尽可能争取更多点击次数/每次点击费用人工出价(智能)/目标每次转化费用3种,那分别什么时候用呢? 当账户刚刚开始投放的时候,可以选择第一/二种,用来获取更多的新客,当账户有了一定的转化数据的时候可以把其中转化次数相对少一些的1-2个广告系列的出价策略更改为“目标每次转化费用”出价,用来增加转化提升询盘数量。转化次数多的广告系列暂时可以不用更换,等更改出价策略的广告系列的转化次数有增加后,可以尝试再修改。 5)广告 1条自适应搜索广告+2条文字广告,尽可能把更多的信息展示客户,增加点击率。那具体的广告语的侧重点是什么呢? 除了产品本身的特点优势外,还是着重于企业的具体产品分类和能够为客户做到哪些服务,例如:专注于各种物体、料位、液位测量仪器生产与研发、为客户提供一体化测量解决方案等。这样进到网站的也基本是寻找相关产品的,从而也进一步提升了转化率。 6)搜索字词 建议日均花费≥200元每周筛选一次,<200元每2周筛选一次。不相关的排除、相关的加到账户中,减少无效点击和花费,这样行业关键字才会越来越精准,做到精准覆盖意向客户。 7)账户广告系列预算 充足的账户预算也至关重要,200-300/天的预算,为什么呢?预算多少其实也就代表着网站流量的多少,之前150/天的预算,账户到下午6点左右就花完了,这样每天就会流失很大一部分客户。广告系列预算可以根据大洲国家的数量分配。数量多的可以分配多一些比如亚洲,预算利用率不足时可以共享预算,把多余的预算放到花费高的系列中。 说完了搜索广告的结构后,接下来就是再营销展示广告了。 2、效果再提升-再营销展示广告结构 因为广告投放覆盖的是曾到达过网站的客户,所以搜索广告的引流精准了,再营销会再抓取并把广告覆盖到因某些原因没有选择我们的客户,做到二次营销。(详细的介绍及操作可以参考文章《精准投放再营销展示广告,就抓住了提升Google营销效果的一大步》) 1)广告组:根据在GA中创建的受众群体导入到账户中。 2)图片: 选择3种产品,每种产品的图片必须提供徽标、横向图片、纵向图片不同尺寸至少1张,最多5张,横向图片可以由多张图片合成一张、可以添加logo和产品名称。 图片设计:再营销展示广告的图片选取从之前的直接选用网站上的产品图,到客户根据我给出的建议设计了独特的产品图片,也提升了0.5%的点击率。 PS: 在广告推广过程中,该客户做过2次产品打折促销活动,信息在图片及描述中曝光,转化率上升1%,如果企业有这方面的计划,可以尝试一下。 YouTube视频链接:如果有YouTube视频的话,建议把视频放在不同的产品页面方便客户实时查看视频,增加真实性,促进询盘及成单,如果视频影响网站打开速度,只在网站标头和logo链接即可。 智能优化建议:谷歌账户会根据推广的数据及状态给出相应的智能优化建议,优化得分≥80分为健康账户分值,每条建议可根据实际情况采纳。 3、网站着陆页 这也是沟通次数很多的问题了,因为即使谷歌为网站引来再多的有质量的客户,如果到达网站后没有看到想要或更多的信息,也是无用功。网站也是企业的第二张脸,做好网站就等于成功一半了。 转变前产品图片模糊、数量少、缺少实物图、工厂库存等体现实力及真实性的图片;产品详情也不是很多,没有足够的竞争力。多次沟通积极配合修改调整后上面的问题全部解决了。网站打开速度保持在3s内、网站的跳出率从之前的80%降到了70%左右、平均页面停留时间也增加了30%。 FAQ:除了正常的网站布局外建议在关于我们或产品详情页添加FAQ,会减少采购商的考虑时间,也会减少因时差导致的与客户失联。如下图所示: 四、账户效果反馈分享篇 1、效果方面 之前每周只有1-2封询盘,现在达到了每周3-5封询盘,确实是提高了不少。 2、询盘成本 从当初的≥1000到现在控制在了100-300左右。 3、转化率 搜索广告+再营销展示广告让网站访客流量得到了充分的利用,增加了1.3%转化率。 就这样,该客户的谷歌账户推广效果有了新的转变,询盘稳定后,又开启了Facebook付费广告,多渠道推广产品,全域赢为目标,产品有市场,这样的模式肯定是如虎添翼。 到此,本次的测控案例就分享完了到这里了,其实部分行业的推广注意事项大方向上都是相通的。催化剂并不难得,找到适合自己的方法~谷歌广告贵在坚持,不是说在一个平台上做的不好就不做了,效果不理想可以改进,改进就能做好。 希望本次的测控案例分享能在某些方面起到帮助作用,在当今大环境下,助力企业增加网站流量及询盘数量,2021祝愿看到这篇文章的企业能够更上一层楼!
2022 年海外社交媒体15 个行业的热门标签
2022 年海外社交媒体15 个行业的热门标签
我们可以在社交媒体上看到不同行业,各种类型的品牌和企业,这些企业里有耳熟能详的大企业,也有刚建立的初创公司。 海外社交媒体也与国内一样是一个广阔的平台,作为跨境企业和卖家,如何让自己的品牌在海外社媒上更引人注意,让更多人看到呢? 在社交媒体上有一个功能,可能让我们的产品、内容被看到,也能吸引更多人关注,那就是标签。 2022年海外社交媒体中不同行业流行哪些标签呢?今天为大家介绍十五个行业超过140多个热门标签,让你找到自己行业的流量密码。 1、银行业、金融业 据 Forrester咨询称,银行业目前已经是一个数万亿的行业,估值正以惊人的速度飙升。银行业正在加速创新,准备加大技术、人才和金融科技方面的投资。 Z世代是金融行业的积极追随者,他们希望能够赶上投资机会。 案例: Shibtoken 是一种去中心化的加密货币,它在社交媒体上分享了一段关于诈骗的视频,受到了很大的关注度,视频告诉观众如何识别和避免陷入诈骗,在短短 20 小时内收到了 1.2K 条评论、3.6K 条转发和 1.14 万个赞。 银行和金融的流行标签 2、娱乐行业 娱乐行业一直都是有着高热度的行业,OTT (互联网电视)平台则进一步提升了娱乐行业的知名度,让每个家庭都能享受到娱乐。 案例: 仅 OTT 视频收入就达 246 亿美元。播客市场也在创造价值 10 亿美元的广告收入。 Netflix 在 YouTube 上的存在则非常有趣,Netflix会发布最新节目预告,进行炒作。即使是非 Netflix 用户也几乎可以立即登录该平台。在 YouTube 上,Netflix的订阅者数量已达到 2220 万。 3、新型微交通 目前,越来越多的人开始关注绿色出行,选择更环保的交通工具作为短距离的出行工具,微型交通是新兴行业,全球市场的复合年增长率为 17.4%,预计到2030 年将达到 195.42 美元。 Lime 是一项倡导游乐设施对人类和环境更安全的绿色倡议。他们会使用#RideGreen 的品牌标签来刺激用户发帖并推广Lime倡议。他们已经通过定期发帖吸引更多人加入微交通,并在社交媒体形成热潮。 4、时尚与美容 到 2025 年,时尚产业将是一个万亿美元的产业,数字化会持续加快这一进程。96% 的美容品牌也将获得更高的社交媒体声誉。 案例: Zepeto 在推特上发布了他们的人物风格,在短短六个小时内就有了自己的品牌人物。 5、旅游业 如果疫情能够有所缓解,酒店和旅游业很快就能从疫情的封闭影响下恢复,酒店业的行业收入可以超过 1900 亿美元,一旦疫情好转,将实现跨越式增长。 案例: Amalfiwhite 在ins上欢迎大家到英国选择他们的酒店, 精彩的Instagram 帖子吸引了很多的关注。 6.健康与健身 健康和健身品牌在社交媒体上发展迅速,其中包括来自全球行业博主的DIY 视频。到 2022 年底,健身行业的价值可以达到 1365.9 亿美元。 案例: Dan The Hinh在 Facebook 页面 发布了锻炼视频,这些健身视频在短短几个小时内就获得了 7300 次点赞和 11000 次分享。 健康和健身的热门标签 #health #healthylifestyle #stayhealthy #healthyskin #healthcoach #fitness #fitnessfreak #fitnessfood #bodyfitness #fitnessjourney 7.食品饮料业 在社交媒体上经常看到的内容类型就是食品和饮料,这一细分市场有着全网超过30% 的推文和60% 的 Facebook 帖子。 案例: Suerte BarGill 在社交媒体上分享调酒师制作饮品的视频,吸引人的视频让观看的人都很想品尝这种饮品。 食品和饮料的热门标签 #food #foodpics #foodies #goodfood #foodgram #beverages #drinks #beverage #drink #cocktails 8. 家居装饰 十年来,在线家居装饰迎来大幅增长,该利基市场的复合年增长率为4%。家居市场现在发展社交媒体也是最佳时机。 案例: Home Adore 在推特上发布家居装饰创意和灵感,目前已经有 220 万粉丝。 家居装饰的流行标签 #homedecor #myhomedecor #homedecorinspo #homedecors #luxuryhomedecor #homedecorlover #home #interiordesign #interiordecor #interiordesigner 9. 房地产 美国有超过200 万的房地产经纪人,其中70% 的人活跃在社交媒体上,加入社交媒体,是一个好机会。 案例: 房地产专家Sonoma County在推特上发布了一篇有关加州一所住宅的豪华图。房地产经纪人都开始利用社交媒体来提升销售额。 房地产的最佳标签 #realestate #realestatesales #realestateagents #realestatemarket #realestateforsale #realestategoals #realestateexperts #broker #luxuryrealestate #realestatelife 10. 牙科 到 2030年,牙科行业预计将飙升至6988 亿美元。 案例: Bridgewater NHS 在推特上发布了一条客户推荐,来建立患者对牙医服务的信任。突然之间,牙科似乎没有那么可怕了! 牙科的流行标签 #dental #dentist #dentistry #smile #teeth #dentalcare #dentalclinic #oralhealth #dentalhygiene #teethwhitening 11. 摄影 摄影在社交媒体中无处不在,持续上传作品可以增加作品集的可信度,当图片参与度增加一倍,覆盖范围增加三倍时,会获得更多的客户。 案例: 著名摄影师理查德·伯纳贝(Richard Bernabe)在推特上发布了他令人着迷的点击。这篇犹他州的帖子获得了 1900 次点赞和 238 次转发。 摄影的热门标签 #photography #photooftheday #photo #picoftheday #photoshoot #travelphotography #portraitphotography #photographylovers #iphonephotography #canonphotography 12. 技术 超过 55% 的 IT 买家会在社交媒体寻找品牌相关资料做出购买决定。这个数字足以说服这个利基市场中的任何人拥有活跃的社交媒体。 案例: The Hacker News是一个广受欢迎的平台,以分享直观的科技新闻而闻名。他们在 Twitter 上已经拥有 751K+ 的追随者。 最佳技术标签 #technology #tech #innovation #engineering #design #business #science #technew s #gadgets #smartphone 13.非政府组织 全球90% 的非政府组织会利用社交媒体向大众寻求支持。社交媒体会有捐赠、公益等组织。 案例: Mercy Ships 通过创造奇迹赢得了全世界的心。这是一篇关于他们的志愿麻醉师的帖子,他们在乌干达挽救了几条生命。 非政府组织的热门标签 #ngo #charity #nonprofit #support #fundraising #donation #socialgood #socialwork #philanthropy #nonprofitorganization 14. 教育 教育行业在过去十年蓬勃发展,借助社交媒体,教育行业有望达到新的高度。电子学习预计将在 6 年内达到万亿美元。 案例: Coursera 是一个领先的学习平台,平台会有很多世界一流大学额课程,它在社交媒体上的可以有效激励人们继续学习和提高技能。 最佳教育标签 #education #learning #school #motivation #students #study #student #children #knowledge #college 15. 医疗保健 疫情进一步证明了医疗保健行业的主导地位,以及挽救生命的力量。到 2022 年,该行业的价值将达到 10 万亿美元。 随着全球健康问题的加剧,医疗保健的兴起也将导致科技和制造业的增长。 案例: CVS Health 是美国领先的药房,积他们的官方账号在社交媒体上分享与健康相关的问题,甚至与知名运动员和著名人物合作,来提高对健康问题的关注度。 医疗保健的热门标签 #healthcare #health #covid #medical #medicine #doctor #hospital #nurse #wellness #healthylifestyle 大多数行业都开始尝试社交媒体,利用社交媒体可以获得更多的关注度和产品、服务的销量,在社交媒体企业和卖家,要关注标签的重要性,标签不仅能扩大帖子的覆盖范围,还能被更多人关注并熟知。 跨境企业和卖家可以通过使用流量高的标签了解当下人们词和竞争对手的受众都关注什么。 焦点LIKE.TG拥有丰富的B2C外贸商城建设经验,北京外贸商城建设、上海外贸商城建设、 广东外贸商城建设、深圳外贸商城建设、佛山外贸商城建设、福建外贸商城建设、 浙江外贸商城建设、山东外贸商城建设、江苏外贸商城建设...... 想要了解更多搜索引擎优化、外贸营销网站建设相关知识, 请拨打电话:400-6130-885。
2024年如何让谷歌快速收录网站页面?【全面指南】
2024年如何让谷歌快速收录网站页面?【全面指南】
什么是收录? 通常,一个网站的页面想要在谷歌上获得流量,需要经历如下三个步骤: 抓取:Google抓取你的页面,查看是否值得索引。 收录(索引):通过初步评估后,Google将你的网页纳入其分类数据库。 排名:这是最后一步,Google将查询结果显示出来。 这其中。收录(Google indexing)是指谷歌通过其网络爬虫(Googlebot)抓取网站上的页面,并将这些页面添加到其数据库中的过程。被收录的页面可以出现在谷歌搜索结果中,当用户进行相关搜索时,这些页面有机会被展示。收录的过程包括三个主要步骤:抓取(Crawling)、索引(Indexing)和排名(Ranking)。首先,谷歌爬虫会抓取网站的内容,然后将符合标准的页面加入索引库,最后根据多种因素对这些页面进行排名。 如何保障收录顺利进行? 确保页面有价值和独特性 确保页面内容对用户和Google有价值。 检查并更新旧内容,确保内容高质量且覆盖相关话题。 定期更新和重新优化内容 定期审查和更新内容,以保持竞争力。 删除低质量页面并创建内容删除计划 删除无流量或不相关的页面,提高网站整体质量。 确保robots.txt文件不阻止抓取 检查和更新robots.txt文件,确保不阻止Google抓取。 检查并修复无效的noindex标签和规范标签 修复导致页面无法索引的无效标签。 确保未索引的页面包含在站点地图中 将未索引的页面添加到XML站点地图中。 修复孤立页面和nofollow内部链接 确保所有页面通过站点地图、内部链接和导航被Google发现。 修复内部nofollow链接,确保正确引导Google抓取。 使用Rank Math Instant Indexing插件 利用Rank Math即时索引插件,快速通知Google抓取新发布的页面。 提高网站质量和索引过程 确保页面高质量、内容强大,并优化抓取预算,提高Google快速索引的可能性。 通过这些步骤,你可以确保Google更快地索引你的网站,提高搜索引擎排名。 如何加快谷歌收录你的网站页面? 1、提交站点地图 提交站点地图Sitemap到谷歌站长工具(Google Search Console)中,在此之前你需要安装SEO插件如Yoast SEO插件来生成Sitemap。通常当你的电脑有了SEO插件并开启Site Map功能后,你可以看到你的 www.你的域名.com/sitemap.xml的形式来访问你的Site Map地图 在谷歌站长工具中提交你的Sitemap 2、转发页面or文章至社交媒体或者论坛 谷歌对于高流量高权重的网站是会经常去爬取收录的,这也是为什么很多时候我们可以在搜索引擎上第一时间搜索到一些最新社媒帖文等。目前最适合转发的平台包括Facebook、Linkedin、Quora、Reddit等,在其他类型的论坛要注意转发文章的外链植入是否违背他们的规则。 3、使用搜索引擎通知工具 这里介绍几个搜索引擎通知工具,Pingler和Pingomatic它们都是免费的,其作用是告诉搜索引擎你提交的某个链接已经更新了,吸引前来爬取。是的,这相当于提交站点地图,只不过这次是提交给第三方。 4、在原有的高权重页面上设置内链 假设你有一些高质量的页面已经获得不错的排名和流量,那么可以在遵循相关性的前提下,适当的从这些页面做几个内链链接到新页面中去,这样可以快速让新页面获得排名
虚拟流量

                                 12个独立站增长黑客办法
12个独立站增长黑客办法
最近总听卖家朋友们聊起增长黑客,所以就给大家总结了一下增长黑客的一些方法。首先要知道,什么是增长黑客? 增长黑客(Growth Hacking)是营销人和程序员的混合体,其目标是产生巨大的增长—快速且经常在预算有限的情况下,是实现短时间内指数增长的最有效手段。增长黑客户和传统营销最大的区别在于: 传统营销重视认知和拉新获客增长黑客关注整个 AARRR 转换漏斗 那么,增长黑客方法有哪些呢?本文总结了12个经典增长黑客方法,对一些不是特别普遍的方法进行了延伸说明,建议收藏阅读。目 录1. SEO 2. 细分用户,低成本精准营销 3. PPC广告 4. Quora 流量黑客 5. 联合线上分享 6. 原生广告内容黑客 7. Google Ratings 8. 邮件营销 9. 调查问卷 10. 用户推荐 11. 比赛和赠送 12. 3000字文案营销1. SEO 查看 AdWords 中转化率最高的关键字,然后围绕这些关键字进行SEO策略的制定。也可以查看 Google Search Console 中的“搜索查询”报告,了解哪些关键字帮助你的网站获得了更多的点击,努力将关键词提升到第1页。用好免费的Google Search Console对于提升SEO有很大帮助。 使用Google Search Console可以在【Links】的部分看到哪个页面的反向连结 (Backlink)最多,从各个页面在建立反向连结上的优劣势。Backlink 的建立在 SEO 上来说是非常重要的! 在 【Coverage】 的部分你可以看到网站中是否有任何页面出现了错误,避免错误太多影响网站表现和排名。 如果担心Google 的爬虫程式漏掉一些页面,还可以在 Google Search Console 上提交网站的 Sitemap ,让 Google 的爬虫程式了解网站结构,避免遗漏页面。 可以使用XML-Sitemaps.com 等工具制作 sitemap,使用 WordPress建站的话还可以安装像Google XML Sitemaps、Yoast SEO 等插件去生成sitemap。2. 细分用户,低成本精准营销 针对那些看过你的产品的销售页面但是没有下单的用户进行精准营销,这样一来受众就会变得非常小,专门针对这些目标受众的打广告还可以提高点击率并大幅提高转化率,非常节约成本,每天经费可能都不到 10 美元。3. PPC广告PPC广告(Pay-per-Click):是根据点击广告或者电子邮件信息的用户数量来付费的一种网络广告定价模式。PPC采用点击付费制,在用户在搜索的同时,协助他们主动接近企业提供的产品及服务。例如Amazon和Facebook的PPC广告。4. Quora 流量黑客 Quora 是一个问答SNS网站,类似于国内的知乎。Quora的使用人群主要集中在美国,印度,英国,加拿大,和澳大利亚,每月有6亿多的访问量。大部分都是通过搜索词,比如品牌名和关键词来到Quora的。例如下图,Quora上对于痘痘肌修复的问题就排在Google搜索相关词的前列。 通过SEMrush + Quora 可以提高在 Google 上的自然搜索排名: 进入SEMrush > Domain Analytics > Organic Research> 搜索 quora.com点击高级过滤器,过滤包含你的目标关键字、位置在前10,搜索流量大于 100 的关键字去Quora在这些问题下发布回答5. 联合线上分享 与在你的领域中有一定知名度的影响者进行线上讲座合作(Webinar),在讲座中传递一些意义的内容,比如一些与你产品息息相关的干货知识,然后将你的产品应用到讲座内容提到的一些问题场景中,最后向用户搜集是否愿意了解你们产品的反馈。 但是,Webinar常见于B2B营销,在B2C领域还是应用的比较少的,而且成本较高。 所以大家在做海外营销的时候不妨灵活转换思维,和领域中有知名度的影响者合作YouTube视频,TikTok/Instagram等平台的直播,在各大社交媒体铺开宣传,是未来几年海外营销的重点趋势。6. 原生广告内容黑客 Native Advertising platform 原生广告是什么?从本质上讲,原生广告是放置在网页浏览量最多的区域中的内容小部件。 简单来说,就是融合了网站、App本身的广告,这种广告会成为网站、App内容的一部分,如Google搜索广告、Facebook的Sponsored Stories以及Twitter的tweet式广告都属于这一范畴。 它的形式不受标准限制,是随场景而变化的广告形式。有视频类、主题表情原生广告、游戏关卡原生广告、Launcher桌面原生广告、Feeds信息流、和手机导航类。7. Google Ratings 在 Google 搜索结果和 Google Ads 上显示产品评分。可以使用任何与Google能集成的电商产品评分应用,并将你网站上的所有评论导入Google系统中。每次有人在搜索结果中看到你的广告或产品页面时,他们都会在旁边看到评分数量。 8. 邮件营销 据外媒统计,80% 的零售行业人士表示电子邮件营销是留住用户的一个非常重要的媒介。一般来说,邮件营销有以下几种类型: 弃单挽回邮件产品补货通知折扣、刮刮卡和优惠券发放全年最优价格邮件通知9. 用户推荐 Refer激励现有用户推荐他人到你的独立站下单。举个例子,Paypal通过用户推荐使他们的业务每天有 7% 到 10%的增长。因此,用户推荐是不可忽视的增长办法。10. 调查问卷 调查问卷是一种快速有效的增长方式,不仅可以衡量用户满意度,还可以获得客户对你产品的期望和意见。调查问卷的内容包括产品体验、物流体验、UI/UX等任何用户购买产品过程中遇到的问题。调查问卷在AARRR模型的Refer层中起到重要的作用,只有搭建好和客户之间沟通的桥梁,才能巩固你的品牌在客户心中的地位,增加好感度。 11. 比赛和赠送 这个增长方式的成本相对较低。你可以让你的用户有机会只需要通过点击就可以赢得他们喜欢的东西,同时帮你你建立知名度并获得更多粉丝。许多电商品牌都以比赛和赠送礼物为特色,而这也是他们成功的一部分。赠送礼物是增加社交媒体帐户曝光和电子邮件列表的绝佳方式。如果您想增加 Instagram 粉丝、Facebook 页面点赞数或电子邮件订阅者,比赛和赠送会创造奇迹。在第一种情况下,你可以让你的受众“在 Instagram 上关注我们来参加比赛”。同样,您可以要求他们“输入电子邮件地址以获胜”。有许多内容可以用来作为赠送礼物的概念:新产品发布/预发售、摄影比赛、节假日活动和赞助活动。12. 3000字文案营销 就某一个主题撰写 3,000 字的有深度博客文章。在文章中引用行业影响者的名言并链接到他们的博文中,然后发邮件让他们知道你在文章中推荐了他们,促进你们之间的互动互推。这种增长办法广泛使用于B2B的服务类网站,比如Shopify和Moz。 DTC品牌可以用这样的增长办法吗?其实不管你卖什么,在哪个行业,展示你的专业知识,分享新闻和原创观点以吸引消费者的注意。虽然这可能不会产生直接的销售,但能在一定程度上影响他们购买的决定,不妨在你的独立站做出一个子页面或单独做一个博客,发布与你产品/服务相关主题的文章。 数据显示,在阅读了品牌网站上的原创博客内容后,60%的消费者对品牌的感觉更积极。如果在博客中能正确使用关键词,还可以提高搜索引擎优化及排名。 比如Cottonbabies.com就利用博文把自己的SEO做得很好。他们有一个针对“布料尿布基础知识”的页面,为用户提供有关“尿布:”主题的所有问题的答案。小贴士:记得要在博客文章末尾链接到“相关产品”哦~本文转载自:https://u-chuhai.com/?s=seo

                                 2021 Shopify独立站推广引流 获取免费流量方法
2021 Shopify独立站推广引流 获取免费流量方法
独立站的流量一般来自两个部分,一种是付费打广告,另外一种就是免费的自然流量,打广告带来的流量是最直接最有效的流量,免费流量可能效果不会那么直接,需要时间去积累和沉淀。但是免费的流量也不容忽视,第一,这些流量是免费的,第二,这些流量是长久有效的。下面分享几个免费流量的获取渠道和方法。 1.SNS 社交媒体营销 SNS 即 Social Network Services,国外最主流的 SNS 平台有 Facebook、Twitter、Linkedin、Instagram 等。SNS 营销就是通过运营这些社交平台,从而获得流量。 SNS 营销套路很多,但本质还是“眼球经济”,简单来说就是把足够“好”的内容,分享给足够“好”的人。好的内容就是足够吸引人的内容,而且这些内容确保不被人反感;好的人就是对你内容感兴趣的人,可能是你的粉丝,也可能是你潜在的粉丝。 如何把你想要发的内容发到需要的人呢?首先我们要确定自己的定位,根据不同的定位在社交媒体平台发布不同的内容,从而自己品牌的忠实粉丝。 1、如果你的定位是营销类的,一般要在社交媒体发布广告贴文、新品推送、优惠信息等。适合大多数电商产品,它的带货效果好,不过需要在短期内积累你的粉丝。如果想要在短期内积累粉丝就不可避免需要使用付费广告。 2、如果你的定位是服务类的,一般要在社交媒体分享售前售后的信息和服务,一般 B2B 企业使用的比较多。 3、如果你的定位是专业类科技产品,一般要在社交媒体分享产品开箱测评,竞品分析等。一般 3C 类的产品适合在社交媒体分享这些内容,像国内也有很多评测社区和网站,这类社区的粉丝一般购买力都比较强。 4、如果你的定位是热点类的,一般要在社交媒体分享行业热点、新闻资讯等内容。因为一般都是热点,所以会带来很多流量,利用这些流量可以快速引流,实现变现。 5、如果你的定位是娱乐类的:一般要在社交媒体分享泛娱乐内容,适合分享钓具、定制、改装类的内容。 2.EDM 邮件营销 很多人对邮件营销还是不太重视,国内一般都是使用在线沟通工具,像微信、qq 比较多,但是在国外,电子邮件则是主流的沟通工具,很多外国人每天使用邮箱的频率跟吃饭一样,所以通过电子邮件营销也是国外非常重要的营销方式。 定期制作精美有吸引力的邮件内容,发给客户,把邮件内容设置成跳转到网站,即可以给网站引流。 3.联盟营销 卖家在联盟平台上支付一定租金并发布商品,联盟平台的会员领取联盟平台分配的浏览等任务,如果会员对这个商品感兴趣,会领取优惠码购买商品,卖家根据优惠码支付给联盟平台一定的佣金。 二、网站SEO引流 SEO(Search Engine Optimization)搜索引擎优化,是指通过采用易于搜索引擎索引的合理手段,使网站各项基本要素适合搜索引擎的检索原则并且对用户更友好,从而更容易被搜索引擎收录及优先排序。 那 SEO 有什么作用嘛?简而言之分为两种,让更多的用户更快的找到他想要的东西;也能让有需求的客户首先找到你。作为卖家,更关心的是如何让有需求的客户首先找到你,那么你就要了解客户的需求,站在客户的角度去想问题。 1.SEO 标签书写规范 通常标签分为标题、关键词、描述这三个部分,首先你要在标题这个部分你要说清楚“你是谁,你干啥,有什么优势。”让人第一眼就了解你,这样才能在第一步就留住有效用户。标题一般不超过 80 个字符;其次,关键词要真实的涵盖你的产品、服务。一般不超过 100 个字符;最后在描述这里,补充标题为表达清楚的信息,一般不超过 200 个字符。 标题+描述 值得注意的是标题+描述,一般会成为搜索引擎检索结果的简介。所以标题和描述一定要完整表达你的产品和品牌的特点和优势。 关键词 关键词的设定也是非常重要的,因为大多数用户购买产品不会直接搜索你的商品,一般都会直接搜索想要购买产品的关键字。关键词一般分为以下四类。 建议目标关键词应该是品牌+产品,这样用户无论搜索品牌还是搜索产品,都能找到你的产品,从而提高命中率。 那如何选择关键词呢?拿我们最常使用的目标关键词举例。首先我们要挖掘出所有的相关关键词,并挑选出和网站自身直接相关的关键词,通过分析挑选出的关键词热度、竞争力,从而确定目标关键词。 注:一般我们都是通过关键词分析工具、搜索引擎引导词、搜索引擎相关搜索、权重指数以及分析同行网站的关键词去分析确定目标关键词。 几个比较常用的关键词分析工具: (免费)MozBar: https://moz.com (付费)SimilarWeb: https://www.similarweb.com/ 2.链接锚文本 什么是锚文本? 一个关键词,带上一个链接,就是一个链接锚文本。带链接的关键词就是锚文本。锚文本在 SEO 过程中起到本根性的作用。简单来说,SEO 就是不断的做锚文本。锚文本链接指向的页面,不仅是引导用户前来访问网站,而且告诉搜索引擎这个页面是“谁”的最佳途径。 站内锚文本 发布站内描文本有利于蜘蛛快速抓取网页、提高权重、增加用户体验减少跳出、有利搜索引擎判断原创内容。你在全网站的有效链接越多,你的排名就越靠前。 3 外部链接什么是外部链接? SEO 中的外部链接又叫导入链接,简称外链、反链。是由其他网站上指向你的网站的链接。 如何知道一个网站有多少外链? 1.Google Search Console 2.站长工具 3.MozBar 4.SimilarWeb 注:低权重、新上线的网站使用工具群发外链初期会得到排名的提升,但被搜索引擎发现后,会导致排名大幅度下滑、降权等。 如何发布外部链接? 通过友情链接 、自建博客 、软文 、论坛 、问答平台发布外链。以下几个注意事项: 1.一个 url 对应一个关键词 2.外链网站与自身相关,像鱼竿和鱼饵,假发和假发护理液,相关却不形成竞争是最好。 3.多找优质网站,大的门户网站(像纽约时报、BBC、WDN 新闻网) 4.内容多样性, 一篇帖子不要重复发 5.频率自然,一周两三篇就可以 6.不要作弊,不能使用隐藏链接、双向链接等方式发布外链 7.不要为了发外链去发外链,“好”的内容才能真正留住客户 4.ALT 标签(图片中的链接) 在产品或图片管理里去编辑 ALT 标签,当用户搜索相关图片时,就会看到图片来源和图片描述。这样能提高你网站关键词密度,从而提高你网站权重。 5.网页更新状态 网站如果经常更新内容的话,会加快这个页面被收录的进度。此外在网站上面还可以添加些“最新文章”版块及留言功能。不要只是为了卖产品而卖产品,这样一方面可以增加用户的粘性,另一方面也加快网站的收录速度。 6.搜索跳出率 跳出率越高,搜索引擎便越会认为你这是个垃圾网站。跳出率高一般有两个原因,用户体验差和广告效果差,用户体验差一般都是通过以下 5 个方面去提升用户体验: 1.优化网站打开速度 2.网站内容整洁、排版清晰合理 3.素材吸引眼球 4.引导功能完善 5.搜索逻辑正常、产品分类明确 广告效果差一般通过这两个方面改善,第一个就是真实宣传 ,确保你的产品是真实的,切勿挂羊头卖狗肉。第二个就是精准定位受众,你的产品再好,推给不需要的人,他也不会去看去买你的产品,这样跳出率肯定会高。本文转载自:https://u-chuhai.com/?s=seo

                                 2022,国际物流发展趋势如何?
2022,国际物流发展趋势如何?
受新冠疫情影响,从2020年下半年开始,国际物流市场出现大规模涨价、爆舱、缺柜等情况。中国出口集装箱运价综合指数去年12月末攀升至1658.58点,创近12年来新高。去年3月苏伊士运河“世纪大堵船”事件的突发,导致运力紧缺加剧,集运价格再创新高,全球经济受到影响,国际物流行业也由此成功出圈。 加之各国政策变化、地缘冲突等影响,国际物流、供应链更是成为近两年行业内关注的焦点。“拥堵、高价、缺箱、缺舱”是去年海运的关键词条,虽然各方也尝试做出了多种调整,但2022年“高价、拥堵”等国际物流特点仍影响着国际社会的发展。 总体上来看,由疫情带来的全球供应链困境会涉及到各行各业,国际物流业也不例外,将继续面对运价高位波动、运力结构调整等状况。在这一复杂的环境中,外贸人要掌握国际物流的发展趋势,着力解决当下难题,找到发展新方向。 国际物流发展趋势 由于内外部因素的影响,国际物流业的发展趋势主要表现为“运力供需矛盾依旧存在”“行业并购整合风起云涌”“新兴技术投入持续增长”“绿色物流加快发展”。 1.运力供需矛盾依旧存在 运力供需矛盾是国际物流业一直存在的问题,近两年这一矛盾不断加深。疫情的爆发更是成了运力矛盾激化、供需紧张加剧的助燃剂,使得国际物流的集散、运输、仓储等环节无法及时、高效地进行连接。各国先后实施的防疫政策,以及受情反弹和通胀压力加大影响,各国经济恢复程度不同,造成全球运力集中在部分线路与港口,船只、人员难以满足市场需求,缺箱、缺舱、缺人、运价飙升、拥堵等成为令物流人头疼的难题。 对物流人来说,自去年下半年开始,多国疫情管控政策有所放松,供应链结构加快调整,运价涨幅、拥堵等难题得到一定缓解,让他们再次看到了希望。2022年,全球多国采取的一系列经济恢复措施,更是缓解了国际物流压力。但由运力配置与现实需求之间的结构性错位导致的运力供需矛盾,基于纠正运力错配短期内无法完成,这一矛盾今年会继续存在。 2.行业并购整合风起云涌 过去两年,国际物流行业内的并购整合大大加快。小型企业间不断整合,大型企业和巨头则择机收购,如Easysent集团并购Goblin物流集团、马士基收购葡萄牙电商物流企业HUUB等,物流资源不断向头部靠拢。 国际物流企业间的并购提速,一方面,源于潜在的不确定性和现实压力,行业并购事件几乎成为必然;另一方面,源于部分企业积极准备上市,需要拓展产品线,优化服务能力,增强市场竞争力,提升物流服务的稳定性。与此同时,由疫情引发的供应链危机,面对供需矛盾严重,全球物流失控,企业需要打造自主可控的供应链。此外,全球航运企业近两年大幅增长的盈利也为企业发起并购增加了信心。 在经历两个年度的并购大战后,今年的国际物流行业并购会更加集中于垂直整合上下游以提升抗冲击能力方面。对国际物流行业而言,企业积极的意愿、充足的资本以及现实的诉求都将使并购整合成为今年行业发展的关键词。 3.新兴技术投入持续增长 受疫情影响,国际物流企业在业务开展、客户维护、人力成本、资金周转等方面的问题不断凸显。因而,部分中小微国际物流企业开始寻求改变,如借助数字化技术降低成本、实现转型,或与行业巨头、国际物流平台企业等合作,从而获得更好的业务赋能。电子商务、物联网、云计算、大数据、区块链、5G、人工智能等数字技术为突破这些困难提供了可能性。 国际物流数字化领域投融资热潮也不断涌现。经过近些年来的发展,处于细分赛道头部的国际物流数字化企业受到追捧,行业大额融资不断涌现,资本逐渐向头部聚集,如诞生于美国硅谷的Flexport在不到五年时间里总融资额高达13亿美元。另外,由于国际物流业并购整合的速度加快,新兴技术的应用就成了企业打造和维持核心竞争力的主要方式之一。因而,2022年行业内新技术的应用或将持续增长。 4.绿色物流加快发展 近年来全球气候变化显著,极端天气频繁出现。自1950年以来,全球气候变化的原因主要来自于温室气体排放等人类活动,其中,CO₂的影响约占三分之二。为应对气候变化,保护环境,各国政府积极开展工作,形成了以《巴黎协定》为代表的一系列重要协议。 而物流业作为国民经济发展的战略性、基础性、先导性产业,肩负着实现节能降碳的重要使命。根据罗兰贝格发布的报告,交通物流行业是全球二氧化碳排放的“大户”,占全球二氧化碳排放量的21%,当前,绿色低碳转型加速已成为物流业共识,“双碳目标”也成行业热议话题。 全球主要经济体已围绕“双碳”战略,不断深化碳定价、碳技术、能源结构调整等重点措施,如奥地利政府计划在2040年实现“碳中和/净零排放”;中国政府计划在2030年实现“碳达峰”,在2060年实现“碳中和/净零排放”。基于各国在落实“双碳”目标方面做出的努力,以及美国重返《巴黎协定》的积极态度,国际物流业近两年围绕“双碳”目标进行的适应性调整在今年将延续,绿色物流成为市场竞争的新赛道,行业内减少碳排放、推动绿色物流发展的步伐也会持续加快。 总之,在疫情反复、突发事件不断,运输物流链阶段性不畅的情况下,国际物流业仍会根据各国政府政策方针不断调整业务布局和发展方向。 运力供需矛盾、行业并购整合、新兴技术投入、物流绿色发展,将对国际物流行业的发展产生一定影响。对物流人来说,2022年仍是机遇与挑战并存的一年。本文转载自:https://u-chuhai.com/?s=seo
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LIKE.TG出海| 推荐出海人最好用的LINE营销系统-云控工具
LIKE.TG出海| 推荐出海人最好用的LINE营销系统-云控工具
在数字化营销的快速发展中,各种社交应用和浏览器为企业提供了丰富的营销系统。其中,LINE营销系统作为一种新兴的社交媒体营销手段,越来越受到企业的重视。同时,比特浏览器作为一种注重隐私和安全的浏览器,也为用户提供了更安全的上网体验。本文LIKE.TG将探讨这两者之间的相互作用,分析它们如何结合为企业带来更高效的营销效果。最好用的LINE营销系统:https://tool.like.tg/免费试用请联系LIKE.TG✈官方客服: @LIKETGAngel一、LINE营销系统概述LINE营销系统是指通过LINE平台开展的一系列营销活动。它利用LINE的即时通讯功能,帮助企业与客户建立紧密的联系。LINE营销系统的核心要素包括:1.群组和频道管理:企业可以创建和管理LINE群组与频道,实时与用户互动,分享产品信息、促销活动和品牌故事。2.用户数据分析:通过分析用户在LINE上的行为,企业能够获取市场洞察,优化产品与服务。3.自动化工具:利用LINE的API,企业可以创建自动化聊天机器人,提供24小时客户服务,提升用户体验。这种系统的优势在于其高效的沟通方式,使品牌能够快速响应客户需求,并通过个性化服务增强客户忠诚度。二、比特浏览器的特点比特浏览器是一款强调用户隐私和安全的浏览器,它在保护用户数据和提供优质上网体验方面具有明显优势。其特点包括:1.隐私保护:比特浏览器通过多重加密保护用户的浏览数据,防止个人信息泄露。2.去中心化特性:用户可以更自由地访问内容,而不受传统浏览器的限制。3.扩展功能:比特浏览器支持多种扩展,能够满足用户个性化的需求,比如广告拦截和隐私保护工具。比特浏览器的设计理念使得它成为那些关注隐私和安全用户的理想选择,这对企业在进行线上营销时,尤其是在数据保护方面提出了更高的要求。三、LINE营销系统与比特浏览器的互补作用 1.用户体验的提升 LINE营销系统的目标是通过即时通讯与用户建立良好的互动关系,而比特浏览器则为用户提供了一个安全的上网环境。当企业通过LINE进行营销时,用户使用比特浏览器访问相关内容,能够享受到更加安全、流畅的体验。这样的组合使得企业能够更好地满足用户的需求,从而提高客户的满意度和忠诚度。 2.数据安全的保障 在数字营销中,数据安全至关重要。企业在使用LINE营销系统收集用户数据时,面临着数据泄露的风险。比特浏览器提供的隐私保护功能能够有效降低这一风险,确保用户在访问企业页面时,个人信息不会被泄露。通过结合这两者,企业不仅能够进行有效的营销,还能够在用户中建立起良好的信任感。 3.营销活动的有效性 LINE营销系统可以帮助企业精准定位目标受众,而比特浏览器则使得用户在浏览营销内容时感受到安全感,这样的结合有助于提升营销活动的有效性。当用户对品牌产生信任后,他们更可能参与活动、购买产品,并进行二次传播,形成良好的口碑效应。四、实际案例分析 为了更好地理解LINE营销系统与比特浏览器的结合效果,我们可以考虑一个成功的案例。一家新兴的电商平台决定通过LINE进行一项促销活动。他们在LINE频道中发布了一系列关于新产品的宣传信息,并引导用户访问专门为此次活动设置的页面。 为了提升用户体验,该平台鼓励用户使用比特浏览器访问这些页面。用户通过比特浏览器访问时,能够享受到更安全的浏览体验,从而更加放心地参与活动。此外,平台还利用LINE的自动化工具,为用户提供实时的咨询和支持。 这一策略取得了显著的效果。通过LIKE.TG官方云控大师,LINE营销系统,电商平台不仅成功吸引了大量用户参与活动,转化率也显著提升。同时,用户反馈表明,他们在使用比特浏览器时感到非常安心,愿意继续关注该品牌的后续活动。五、营销策略的优化建议 尽管LINE营销系统和比特浏览器的结合能够带来诸多优势,但在实际应用中,企业仍需注意以下几点:1.用户教育:许多用户可能对LINE和比特浏览器的结合使用不够了解,因此企业应提供必要的教育和培训,让用户了解如何使用这两种工具进行安全的在线互动。2.内容的多样性:为了吸引用户的兴趣,企业需要在LINE营销中提供多样化的内容,包括视频、图文和互动问答等,使用户在使用比特浏览器时有更丰富的体验。3.持续的效果评估:企业应定期对营销活动的效果进行评估,了解用户在使用LINE和比特浏览器时的反馈,及时调整策略以提升活动的有效性。六、未来展望 随着数字营销的不断演进,LINE营销系统和比特浏览器的结合将会变得越来越重要。企业需要不断探索如何更好地利用这两者的优势,以满足日益增长的用户需求。 在未来,随着技术的发展,LINE营销系统可能会集成更多智能化的功能,例如基于AI的个性化推荐和精准广告投放。而比特浏览器也可能会进一步加强其隐私保护机制,为用户提供更为安全的上网体验。这些发展将为企业带来更多的营销机会,也将改变用户与品牌之间的互动方式。 在数字化营销的新时代,LINE营销系统和比特浏览器的结合为企业提供了一个全新的营销视角。通过优化用户体验、保障数据安全和提升营销活动的有效性,企业能够在激烈的市场竞争中占据优势。尽管在实施过程中可能面临一些挑战,但通过合理的策略,企业将能够充分利用这一结合,最终实现可持续的发展。未来,随着技术的不断进步,这一领域将继续为企业提供更多的机会与挑战。免费使用LIKE.TG官方:各平台云控,住宅代理IP,翻译器,计数器,号段筛选等出海工具;请联系LIKE.TG✈官方客服: @LIKETGAngel想要了解更多,还可以加入LIKE.TG官方社群 LIKE.TG生态链-全球资源互联社区。
LIKE.TG出海|kookeey:团队优选的住宅代理服务
LIKE.TG出海|kookeey
团队优选的住宅代理服务
在当今互联网时代, 住宅代理IP 已成为许多企业和团队绕不开的技术工具。为了确保这些代理的顺利运行,ISP白名单的设置显得尤为重要。通过将 住宅代理IP 添加至白名单,可以有效提升代理连接的稳定性,同时避免因网络限制而引发的不必要麻烦。isp whitelist ISP白名单(Internet Service Provider Whitelist)是指由网络服务提供商维护的一组信任列表,将信任的IP地址或域名标记为无需进一步检查或限制的对象。这对使用 住宅代理IP 的用户尤其重要,因为某些ISP可能对陌生或不常见的IP流量采取防护措施,从而影响网络访问的速度与体验。二、设置isp whitelist(ISP白名单)的重要性与优势将 住宅代理IP 添加到ISP白名单中,不仅能优化网络连接,还能带来以下显著优势:提升网络连接稳定性ISP白名单能够有效避免IP地址被错误标记为异常流量或潜在威胁,这对使用 住宅代理IP 的团队而言尤为重要。通过白名单设置,网络通信的中断率将显著降低,从而保证代理服务的连续性。避免验证环节在某些情况下,ISP可能会针对未知的IP地址触发额外的验证流程。这些验证可能导致操作延迟,甚至直接限制代理的功能。而通过将 住宅代理IP 纳入白名单,团队可以免除不必要的干扰,提升工作效率。增强数据传输的安全性白名单机制不仅可以优化性能,还能确保流量来源的可信度,从而降低网络攻击的风险。这对于依赖 住宅代理IP 处理敏感数据的企业来说,尤为重要。三、如何将住宅代理IP添加到ISP白名单添加 住宅代理IP 到ISP白名单通常需要以下步骤:确认代理IP的合法性在向ISP提交白名单申请前,确保代理IP来源合法,且服务商信誉良好。像 LIKE.TG 提供的住宅代理IP 就是一个值得信赖的选择,其IP资源丰富且稳定。联系ISP提供支持与ISP的技术支持团队联系,说明将特定 住宅代理IP 添加到白名单的需求。多数ISP会要求填写申请表格,并提供使用代理的具体场景。提交必要文档与信息通常需要提交代理服务的基本信息、IP范围,以及使用目的等细节。像 LIKE.TG 平台提供的服务,可以帮助用户快速获取所需的相关材料。等待审核并测试连接在ISP完成审核后,测试 住宅代理IP 的连接性能,确保其运行无异常。四、为何推荐LIKE.TG住宅代理IP服务当谈到住宅代理服务时, LIKE.TG 是业内的佼佼者,其提供的 住宅代理IP 不仅数量丰富,而且连接速度快、安全性高。以下是选择LIKE.TG的几大理由:全球覆盖范围广LIKE.TG的 住宅代理IP 覆盖全球多个国家和地区,无论是本地化业务需求,还是跨国访问,都能轻松满足。高效的客户支持无论在IP分配还是白名单设置中遇到问题,LIKE.TG都能提供及时的技术支持,帮助用户快速解决难题。灵活的定制服务用户可根据自身需求,选择合适的 住宅代理IP,并通过LIKE.TG的平台进行灵活配置。安全与隐私保障LIKE.TG对数据安全有严格的保护措施,其 住宅代理IP 服务采用先进的加密技术,确保传输过程中的隐私无忧。五、ISP白名单与住宅代理IP的完美结合将 住宅代理IP 纳入ISP白名单,是提升网络效率、保障数据安全的关键步骤。无论是出于业务需求还是隐私保护,选择优质的代理服务商至关重要。而 LIKE.TG 提供的住宅代理服务,以其卓越的性能和优质的用户体验,成为团队和企业的理想选择。如果您正在寻找稳定、安全的 住宅代理IP,并希望与ISP白名单功能完美结合,LIKE.TG无疑是值得信赖的合作伙伴。LIKE.TG海外住宅IP代理平台1.丰富的静/动态IP资源/双ISP资源提供大量可用的静态和动态IP,低延迟、独享使用,系统稳定性高达99%以上,确保您的网络体验流畅无忧。2.全球VPS服务器覆盖提供主要国家的VPS服务器,节点资源充足,支持低延迟的稳定云主机,为您的业务运行保驾护航。3.LIKE.TG全生态支持多平台多账号防关联管理。无论是海外营销还是账号运营,都能为您打造最可靠的网络环境。4.全天候技术支持真正的24小时人工服务,专业技术团队随时待命,为您的业务需求提供个性化咨询和技术解决方案。免费使用LIKE.TG官方:各平台云控,住宅代理IP,翻译器,计数器,号段筛选等出海工具;请联系LIKE.TG✈官方客服: @LIKETGAngel想要了解更多,还可以加入LIKE.TG官方社群 LIKE.TG生态链-全球资源互联社区/联系客服进行咨询领取官方福利哦!
LIKE.TG出海|Line智能云控拓客营销系统   一站式营销平台助您实现海外推广
LIKE.TG出海|Line智能云控拓客营销系统 一站式营销平台助您实现海外推广
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