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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.
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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.
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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
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.
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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
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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
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
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;
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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
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.
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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.
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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.
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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.
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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.
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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.
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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!
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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
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.
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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.
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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.
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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.
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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
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.
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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.
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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.
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Share your experience of connecting DynamoDB to S3 in the comments section below!
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.
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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.
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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
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
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
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
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.
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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.
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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.
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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.
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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
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.
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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.
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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
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!
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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.
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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.
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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
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.
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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!
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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.
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Share your experience of loading data from SQS to S3 in the comment section below.
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
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
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
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.
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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.
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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
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.
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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 !
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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
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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
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
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.
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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.
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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.
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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
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.
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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.
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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.
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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
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
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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.