When you use different kinds of databases, there would be a need to migrate data between them frequently. A specific use case that often comes up is the transfer of data from your transactional database to your data warehouse such as transfer/copy data from DynamoDB to Redshift. This article introduces you to AWS DynamoDB and Redshift. It also provides 4 methods (with detailed instructions) that you can use to migrate data from AWS DynamoDB to Redshift.Loading Data From Dynamo DB To Redshift Method 1: DynamoDB to Redshift Using LIKE.TG Data LIKE.TG Data, an Automated No-Code Data Pipeline can transfer data from DynamoDB to Redshift and provide you with a hassle-free experience. You can easily ingest data from the DynamoDB database using LIKE.TG ’s Data Pipelines and replicate it to your Redshift account without writing a single line of code. LIKE.TG ’s end-to-end data management service automates the process of not only loading data from DynamoDB but also transforming and enriching it into an analysis-ready form when it reaches Redshift. Get Started with LIKE.TG for Free LIKE.TG supports direct integrations with DynamoDB and 150+ Data 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. Method 2: DynamoDB to Redshift Using Redshift’s COPY Command This method operates on the Amazon Redshift’s COPY command which can accept a DynamoDB URL as one of the inputs. This way, Redshift can automatically manage the process of copying DynamoDB data on its own. This method is suited for one-time data transfer. Method 3: DynamoDB to Redshift Using AWS Data Pipeline This method uses AWS Data Pipeline which first migrates data from DynamoDB to S3. Afterward, data is transferred from S3 to Redshift using Redshift’s COPY command. However, it can not transfer the data directly from DynamoDb to Redshift. Method 4: DynamoDB to Redshift Using Dynamo DB Streams This method leverages the DynamoDB Streams which provide a time-ordered sequence of records that contains data modified inside a DynamoDB table. This item-level record of DynamoDB’s table activity can be used to recreate a similar item-level table activity in Redshift using some client application that is capable of consuming this stream. This method is better suited for regular real-time data transfer. Methods to Copy Data from DynamoDB to Redshift Copying data from DynamoDB to Redshift can be accomplished in 4 ways depending on the use case.Following are the ways to copy data from DynamoDB to Redshift: Method 1: DynamoDB to Redshift Using LIKE.TG Data Method 2: DynamoDB to Redshift Using Redshift’s COPY Command Method 3: DynamoDB to Redshift Using AWS Data Pipeline Method 4: DynamoDB to Redshift Using DynamoDB Streams Each of these 4 methods is suited for the different use cases and involves a varied range of effort. Let’s dive in. Method 1: DynamoDB to Redshift Using LIKE.TG Data LIKE.TG Data, an Automated No-code Data Pipelinehelps you to directly transfer yourAWS DynamoDBdata toRedshiftin real-time in a completely automated manner. LIKE.TG ’s fully managed pipeline uses DynamoDB’sdata streamsto supportChange Data Capture (CDC)for its tables. LIKE.TG also facilitates DynamoDB’s data replication to manage the ingestion information viaAmazon DynamoDB StreamsAmazon Kinesis Data Streams. Here are the 2 simple steps you need to use to move data from DynamoDB to Redshift using LIKE.TG : Step 1) Authenticate Source: Connect your DynamoDB account as a source for LIKE.TG by entering a unique name for LIKE.TG Pipeline, AWS Access Key, AWS Secret Key, and AWS Region. This is shown in the below image. Step 2) Configure Destination: Configure the Redshift data warehouse as the destination for your LIKE.TG Pipeline. You have to provide, warehouse name, database password, database schema, database port, and database username. This is shown in the below image. That is it! LIKE.TG will take care of reliably moving data from DynamoDB to Redshift with no data loss. Sign Up for a 14 day free Trial Here are more reasons to try LIKE.TG : Schema Management: LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to your Redshift schema. 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. LIKE.TG also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. 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 Monitoring: LIKE.TG allows you to monitor the data flow and check where your data is at a particular point in time. With continuous real-time data movement, LIKE.TG allows you to combine Amazon DynamoDB data along with your other data sources and seamlessly load it to Redshift with a no-code, easy-to-setup interface. Method 2: DynamoDB to Redshift Using Redshift’s COPY Command This is by far the simplest way to copy a table from DynamoDB stream to Redshift. Redshift’s COPY command can accept a DynamoDB URL as one of the inputs and manage the copying process on its own. The syntax for the COPY command is as below. copy <target_tablename> from 'dynamodb://<source_table_name>' authorization read ratio '<integer>'; For now, let’s assume you need to move product_details_v1 table from DynamoDB to Redshift (to a particular target table) named product_details_tgt. The command to move data will be as follows. COPY product_details_v1_tgt from dynamodb://product_details_v1 credentials ‘aws_access_key_id = <access_key_id>;aws_secret_access_key=<secret_access_key> readratio 40; The “readratio” parameter in the above command specifies the amount of provisioned capacity in the DynamoDB instance that can be used for this operation. This operation is usually a performance-intensive one and it is recommended to keep this value below 50% to avoid the source database getting busy. Limitations of Using Redshift’s Copy Command to Load Data from DynamoDB to Redshift The above command may look easy, but in real life, there are multiple problems that a user needs to be careful about while doing this. A list of such critical factors that should be considered is given below. DynamoDB and Redshift follow different sets of rules for their table names. While DynamoDB allows for the use of up to 255 characters to form the table name, Redshift limits it to 127 characters and prohibits the use of many special characters, including dots and dashs. In addition to that, Redshift table names are case-insensitive. While copying data from DynamoDB to Redshift, Redshift tries to map between DynamoDB attribute names and Redshift column names. If there is no match for a Redshift column name, it is populated as empty or NULL depending on the value of EMPTYASNULL parameter configuration parameter in the COPY command. All the attribute names in DynamoDB that cannot be matched to column names in Redshift are discarded. At the moment, the COPY command only supports STRING and NUMBER data types in DynamoDB. The above method works well when the copying operation is a one-time operation. Method 3: DynamoDB to Redshift Using AWS Data Pipeline AWS Data Pipeline is Amazon’s own service to execute the migration of data from one point to another point in the AWS Ecosystem. Unfortunately, it does not directly provide us with an option to copy data from DynamoDB to Redshift but gives us an option to export DynamoDB data to S3. From S3, we will need to used a COPY command to recreate the table in S3. Follow the steps below to copy data from DynamoDB to Redshift using AWS Data Pipeline: Create an AWS Data pipeline from the AWS Management Console and select the option “Export DynamoDB table to S3” in the source option as shown in the image below. A detailed account of how to use the AWS Data Pipeline can be found in the blog post. Once the Data Pipeline completes the export,use the COPY command with the source path as the JSON file location. The COPY command is intelligent enough to autoload the table using JSON attributes. The following command can be used to accomplish the same. COPY product_details_v1_tgt from s3://my_bucket/product_details_v1.json credentials ‘aws_access_key_id = <access_key_id>;aws_secret_access_key=<secret_access_key> Json = ‘auto’ In the avove command, product_details_v1.json is the output of AWS Data Pipeline execution. Alternately instead of the “auto” argument, a JSON file can be specified to map the JSON attribute names to Redshift columns, in case those two are not matching. Method 4: DynamoDB to Redshift Using DynamoDB Streams The above methods are fine if the use case requires only periodic copying of the data from DynamoDB to Redshift. There are specific use cases where real-time syncing from DDB to Redshift is needed. In such cases, DynamoDB’s Streams feature can be exploited to design a streaming copy data pipeline. DynamoDB Stream provides a time-ordered sequence of records that correspond to item level modification in a DynamoDB table. This item-level record of table activity can be used to recreate an item-level table activity in Redshift using a client application that can consume this stream. Amazon has designed the DynamoDB Streams to adhere to the architecture of Kinesis Streams. This means the customer just needs to create a Kinesis Firehose Delivery Stream to exploit the DynamoDB Stream data. The following are the broad set of steps involved in this method: Enable DynamoDB Stream in the DynamoDB console dashboard. Configure a Kinesis Firehose Delivery Stream to consume the DynamoDB Stream to write this data to S3. Implement an AWS Lambda Function to buffer the data from the Firehose Delivery Stream, batch it and apply the required transformations. Configure another Kinesis Data Firehose to insert this data to Redshift automatically. Even though this method requires the user to implement custom functions, it provides unlimited scope for transforming the data before writing to Redshift. Conclusion The article provided you with 4 different methods that you can use to copy data from DynamoDB to Redshift. Since DynamoDB is usually used as a transactional database and Redshift as a data warehouse, the need to copy data from DynamoDB is very common. If you’re interested in learning about the differences between the two, take a look at the article: Amazon Redshift vs. DynamoDB. Depending on whether the use case demands a one-time copy or continuous sync, one of the above methods can be chosen. Method 2 and Method 2 are simple in implementation but come along with multiple limitations. Moreover, they are suitable only for one-time data transfer between DynamoDB and Redshift. The method using DynamoDB Streams is suitable for real-time data transfer, but a large number of configuration parameters and intricate details have to be considered for its successful implementation LIKE.TG Data provides an Automated No-code Data Pipeline that empowers you to overcome the above-mentioned limitations. You can leverage LIKE.TG to seamlessly transfer data from DynamoDB to Redshift in real-time without writing a single line of code. Learn more about 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 firsthand. Checkout the LIKE.TG pricing to choose the best plan for you. Share your experience of copying data from DynamoDB to Redshift in the comment section below!

As your company grows and starts generating terabytes of complex data, and you have data stored in different sources. That’s when you have to incorporate a data warehouse like BigQuery into your data architecture for migrating data from Google Sheets to BigQuery. Sieving through terabytes of data on sheets is quite a monotonous endeavor and places a ceiling on what is achievable when it comes to data analysis. At this juncture incorporating a data warehouse like BigQuery becomes a necessity.In this blog post, we will be covering extensively how you can move data from Google Sheets to BigQuery. Methods to Connect Google Sheets to BigQuery Now that we have built some background information on the spreadsheets and why it is important to incorporate BigQuery into your data architecture, next we will look at how to import data. Here, it is assumed that you already have a GCP account. If you don’t already have one, you can set it up. Google offers new users $300 free credits for a year. You can always use these free credits to get a feel of GCP and access BigQuery. Method 1: Using LIKE.TG to Move Data from Google Sheets to BigQuery 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. Using a fully managed platform likeLIKE.TG you bypass all the aforementioned complexities and (supports as a free data source) import Google Sheet to BigQuery in just a few mins. You can achieve this in 2 simple steps: Step 1: Configure Google Sheets as a source, by entering the Pipeline Name and the spreadsheet you wish to replicate. Step 2:Connect to your BigQuery account and start moving your data from Google Sheets to BigQuery by providingthe project ID, dataset ID, Data Warehouse name, and GCS bucket. For more details, Check out: Google Sheets Source Connector BigQuery Destinations Connector Key features of LIKE.TG are, 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. Method 2: Using BigQuery Connector to Move Data from Google Sheets to BigQuery You can easily upload using BigQuery’s data connector. The steps below illustrate how: Step 1: Log in to your GCP console and Navigate to the BigQuery UI using the hamburger menu. Step 2: Inside BigQuery, select ‘Create Dataset’. Step 3: After creating the dataset, next up we create a BigQuery table that will contain our incoming data from sheets.To create BigQuery table from Google Sheet, click on ‘Create a table.’ In the ‘create a table‘ tab, select Drive. Step 4: Under the source window, choose Google Drive as your source and populate the Select Drive URL tab with the URL from your Google Sheet. You can select either CSV or Sheets as the format. Both formats allow you to select the auto-detect schema. You could also specify the column names and data types. Step 5: Fill in the table name and select ‘Create a table.’ With your Google Sheets linked to your Google BigQuery, you can always commit changes to your sheet and it will automatically appear in Google BigQuery. Step 6: Now that we have data in BigQuery, we can perform SQL queries on our ingested data. The following image shows a short query we performed on the data in BigQuery. Method 3: Using Sheets Connector to Move Data from Google Sheets to BigQuery This method to upload Google Sheet to BigQuer is only available for Business, Enterprise, or Education G Suite accounts. This method allows you to save your SQL queries directly into your Google Sheets. Steps to using the Sheet’s data connector are highlighted below with the help of a public dataset: Step 1: For starters, open or create a Google Sheets spreadsheet. Step 2: Next, click on Data > Data Connectors > Connect to BigQuery. Step 3: Click Get Connected, and select a Google Cloud project with billing enabled. Step 4: Next, click on Public Datasets. Type Chicago in the search box, and then select the Chicago_taxi_trips dataset. From this dataset choose the taxi_trips table and then click on the Connect button to finish this step. This is what your Google Sheets spreadsheet will look like: You can now use this spreadsheet to create formulas, charts, and pivot tables using various Google Sheets techniques. Managing Access and Controlling Share Settings It is pertinent that your data is protected across both Sheet and BigQuery, hence you can manage who has access to both the sheet and BigQuery. To do this; all you need to do is create a Google Group to serve as an access control group. By clicking the share icon on sheets, you can grant access to which of your team members can edit, view or comment. Whatever changes are made here will also be replicated on BigQuery. This will serve as a form of IAM for your data set. Limitations of using Sheets Connector to Connect Google Sheets to BigQuery In this blog post, we covered how you can incorporate BigQuery into Google Sheets in two ways so far. Despite the immeasurable benefits of the process, it has some limitations. This process cannot support volumes of data greater than 10,000 rows in a single spreadsheet. To make use of the sheets data connector for BigQuery, you need to operate a Business, Enterprise, or Education G suite account. This is an expensive option. Before wrapping up, let’s cover some basics. Introduction to Google Sheets Spreadsheets are electronic worksheets that contain rows and columns which users can input, manage and carry out mathematical operations on their data. It gives users the unique ability to create tables, charts, and graphs to perform analysis. Google Sheets is a spreadsheet program that is offered by Google as a part of their Google Docs Editor suite. This suite also includes Google Drawings, Google Slides, Google Forms, Google Docs, Google Keep, and Google Sites. Google Sheets gives you the option to choose from a vast variety of schedules, budgets, and other pre-made spreadsheets that are designed to make your work that much better and your life easier. Here are a few key features of Google Sheets In Google Sheets, all your changes are saved automatically as you type. You can use revision history to see old versions of the same spreadsheet. It is sorted by the people who made the change and the date. It also allows you to get instant insights with its Explore panel. It allows you to get an overview of data from a selection of pre-populated charts to informative summaries to choose from. Google Sheets allows everyone to work together in the same spreadsheet at the same time. You can create, access, and edit your spreadsheets wherever you go- from your tablet, phone, or computer. Introduction to BigQuery Google BigQuery is a data warehouse technology designed by Google to make data analysis more productive by providing fast SQL-querying for big data. The points below reiterate how BigQuery can help improve our overall data architecture: When it comes to Google BigQuery size is never a problem. You can analyze up to 1TB of data and store up to 10GB for free each month. BigQuery gives you the liberty to focus on analytics while fully abstracting all forms of infrastructure, so you can focus on what matters. Incorporating BigQuery into your architecture will open you to the services on GCP(Google Cloud Platform). GCP provides a suite of cloud services such as data storage, data analysis, and machine learning. With BigQuery in your architecture, you can apply Machine learning to your data by using BigQuery ML. If you and your team are collaborating on google sheets you can make use of Google Data Studio to build interactive dashboards and graphical rendering to better represent the data. These dashboards are updated as data is updated on the spreadsheet. BigQuery offers a strong security regime for all its users. It offers a 99.9% service level agreement and strictly adheres to privacy shield principles. GCP provides its users with Identity and Access Management (IAM), where you as the main user can decide the specific data each member of your team can access. BigQuery offers an elastic warehouse model that scales automatically according to your data size and query complexity. Additional Resources on Google Sheets to Bigquery Move Data from Excel to Bigquery Conclusion This blog talks about the 3 different methods you can use to move data from Google Sheets to BigQuery in a seamless fashion. In addition to Google Sheets, LIKE.TG can move data from a variety ofFree Paid Data Sources(Databases, Cloud Applications, SDKs, and more). LIKE.TG ensures that your data is consistently and securely moved from any source to BigQuery in real-time.

MariaDB and MySQL are two widely popular relational databases that boast many of the largest enterprises as their clientele. Both MariaDB and MySQL are available in two versions – A community-driven version and an enterprise version. However, the distribution of features and development processes in the community and enterprise versions of MySQL and MariaDB differ from each other. Even though MariaDB claims itself as a drop-in replacement for MySQL, because of the terms of licensing and enterprising support contracts, many organizations migrate between these two according to their policy changes. This blog post will cover the details of how to move data from MariaDB to MySQL. What is MariaDB? MariaDB is a RDBMS built on SQL, created by the professionals behind the development of MySQL intended to provide technical efficiency and versatility. You can use this database for many use cases, which include data warehousing, and managing your data. Its relational nature will be helpful for you. And, the open-source community will provide you with the resources required. What is MySQL? MySQL is one of the renowned open source relational database management systems. You can store and arrange data in structured formats in tables with columns and rows. You can define, query, manage, and manipulate your data using SQL. You can use MySQL to develop websites, and applications. Examples of companies who used this are Uber, Airbnb, Pinterest, and Shopify. They use MySQL for their database management requirements because of its versatility and capabilities to in manage large operations. Methods to Integrate MariaDB with MySQL Method 1: Using LIKE.TG Data to Connect MariaDB to MySQL A fully managed, No-Code Data Pipeline platform like LIKE.TG Data allows you to seamlessly migrate your data from MariaDB to MySQL in just two easy steps. No specialized technical expertise is required to perform the migration. Method 2: Using Custom Code to Connect MariaDB to MySQL Use mysqldump to migrate your data from MariaDB to MySQL by writing a couple of commands mentioned in the blog. However this is a costly operation that can also overload the primary database. Method 3: Using MySQL Workbench You can also migrate your data from MariaDB to MySQL using the MySQL Migration Wizard. However, it has limitations on the size of migrations that it can handle effectively, and as a result, it cannot handle very large datasets. Get Started with LIKE.TG for Free Method 1: Using LIKE.TG Data to Connect MariaDB to MySQL The steps involved are, Step 1: Configure MariaDB as Source Step 2: Configure MySQL as Destination Check out why LIKE.TG is the Best: 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. 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.’ 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. Get Started with LIKE.TG for Free Method 2: Using Custom Code to Connect MariaDB to MySQL Since both databases provide the same underlying tools, it is very easy to copy data from MariaDB to MySQL. The following steps detail how to accomplish this. Step 1: From the client machine, use the below command to create a complete dump of the database in MariaDB. mysqldump -u username -p database_name > source_dump.sql This command creates a source_dump.sql file. Step 2: Move the file to a machine that can access the target MySQL database. If the same machine has access to the target database, this step is not relevant. Step 3: Log in as root to the target MySQL database mysql -u root -p password Step 4: In the MySQL shell, execute the below command to create a database. CREATE DATABASE target_database;Where target_database is the name of the database to which data is to be imported. Step 5: Exit the MySQL shell and go to the location where the source_dump.sql is stored. Step 6: Execute the below command to load the database from the dump file. mysql -u username -p new_database < source_dump.sql That concludes the process. The target database is now ready for use and this can be verified by logging in to the MySQL shell and executing a SHOW TABLES command. Even though this approach provides a simple way for a one-off copy operation between the two databases, this method has a number of limitations. Let’s have a look at the limitations of this approach. MariaDB to MySQL: Limitations of Custom Code Approach In most cases, the original database will be online while the customer attempts to copy the data. mysqldump command is a costly execution and can lead to the primary database being unavailable or slow during the process. While the mysqldump command is being executed, new data could come in resulting in some leftover data. This data needs to be handled separately. This approach works fine if the copying operation is a one-off process. In some cases, organizations may want to maintain an exact running replica of MariaDB in MySQL and then migrate. This will need a complex script that can use the binary logs to create a replica. Even though MariaDB claims itself as a drop-in replacement, the development has been diverging now and there are many incompatibilities between versions as described here. This may lead to problems while migrating using the above approach. Migrate from MariaDB to MySQLGet a DemoTry itMigrate from MariaDB to PostgreSQLGet a DemoTry it Method 3: Using MySQL Workbench In MySQL Workbench, navigate yourself to Database> Migrate to initiate the migration wizard. Go to Overview page -> select Open ODBC Manager. This is done to make sure the ODBC drive for MySQL Server is installed. If not, useMySQL installer used to install MySQL Workbench for installing it. Select Start Migration. Click and specify details on source database -> test the connection -> select Next. Configure the target database details and verify connection. Get the wizard extracting the schema list from the source server -> select the schema for migrating. The migration will begin once you mention the objects you want to migrate on the Source Objects page. Make edits in the generated SQL for all objects -> edit migration issues, or change the name of the target object and columns on the View drop-down of Manual Edit. Go to the next page -> choose create schema in target RDBMS -> Give it sometime to finish the creation. And check the created objects on the Create Target Results page. In the Data Transfer Settings page, configure data migration -> Select Next to move your data. Check the migration report after the process -> select Finish to close the wizard. You can check the consistency of source data and schema by logging into the target database. Also, check if the table and row counts match. SELECT COUNT (*) FROM table_name; Get MySQL row count of tables in your database. SELECT table_name, table_rows FROM information_schema.tables WHERE table_schema = 'classicmodels' ORDER BY table_name; 14. Check the database size. SELECT TABLE_SCHEMA AS `Database`, TABLE_NAME AS `Table`, ROUND((DATA_LENGTH + INDEX_LENGTH) / 1024 / 1024) AS `Size (MB)` FROM information_schema.TABLES GROUP BY table_schema; Understand the size of the table. SELECT table_name AS "Table", ROUND(((data_length + index_length) / 1024 / 1024), 2) AS "Size (MB)" FROM information_schema.TABLES WHERE table_schema = "database_name" ORDER BY (data_length + index_length) DESC; Limitations of using MySQL Workbench to Migrate MariaDB to MySQL: Size Constraints: MySQL workbench has limitations on the size of migrations that it can handle effectively. It cannot be used for very large databases. Limited Functionality: It cannot deal with complex data structures efficiently. It requires manual interventions or additional tools to do so when using MySQL workbench. Use Cases of MariaDB to MySQL Migration MySQL is suitable for heavily trafficked websites and mission-critical applications. MySQL can handle terabyte-sized databases and also supports high-availability database clustering. When you migrate MariaDB to MySQL, you can manage databases of websites and applications with high traffic. Popular applications that use the MySQL database include TYPO3, MODx, Joomla, WordPress, phpBB, MyBB, and Drupal. MySQL is one of the most popular transactional engines for eCommerce platforms. Thus, when you convert MariaDB to MySQL, it becomes easy to use to manage customer data, transactions, and product catalogs. When you import MariaDB to MySQL, it assists you in fraud detection. MySQL helps to analyze transactions, claims etc. in real-time, along with trends or anomalous behavior to prevent fraudulent activities. Learn More About: How to Migrate MS SQL to MySQL in 3 Methods Migrate Postgres to MySQL Connecting FTP to MySQL Conclusion This blog explained two methods that you can use to import MariaDB to MySQL. The manual custom coding method provides a simple approach for a one-off migration between MariaDB and MySQL. Among the methods provided, determining which method is to be used depends on your use case. You can go for an automated data pipeline platform if you want continuous or periodic copying operations. Sign Up for a 14-day free trial FAQ on MariaDB to MySQL How do I switch from MariaDB to MySQL? You can transfer your data from MariaDB to MySQL using custom code or automated pipeline platforms like LIKE.TG Data. How to connect MariaDB to MySQL? You can do this by using custom codes. The steps include:1. Create a Dump of MariaDB2. Log in to MySQL as a Root User3. Create a MySQL Database4. Restore the Data5. Verify and Test How to upgrade MariaDB to MySQL? Upgrading from MariaDB to MySQL would involve fully backing the MariaDB databases. Afterward, uninstall MariaDB, install MySQL, and restore from the created backup. Be sure that the MySQL version supports all features used in your setup. Is MariaDB compatible with MySQL? MariaDB’s data files are generally binary compatible with those from the equivalent MySQL version.

Choosing the right data integration tool can be tricky, with many options available today. If you’re not clear on what you need, you might end up making the wrong choice.That’s why it’s crucial to have essential details and information, such as what factors to consider and how to choose the best data integration tools, before making a decision. In this article, I have compiled a list of 15 tools to help you choose the correct data integration tool that meets all your requirements. You’ll also learn about the benefits of these tools and the key factors to consider when selecting these tools. Let’s dive in! Understanding Data Integration Data integration is merging data from diverse sources to create a cohesive, comprehensive dataset that gives you a unified view. By consolidating data across multiple sources, your organization can discover insights and patterns that might remain hidden while examining data from individual sources alone. List of 15 Best Data Integration Tools in 2024 With such a large number of products on the market, finding the right Data Integration Tools for a company’s needs can be tough. Here’s an overview of seven of the most popular and tried-out Database Replication solutions. These are the top Data Integration Tools used widely in the market today. 1. LIKE.TG Data With LIKE.TG , you get a growing library of over 150 plug-and-play connectors, including all your SaaS applications, databases, and file systems. You can also choose from destinations like Snowflake, BigQuery, Redshift, Databricks, and Firebolt. Data integrations are done effortlessly in near real-time with an intuitive, no-code interface. It is scalable and cost-effectively automates a data pipeline, ensuring flexibility to meet your needs. Key features of LIKE.TG Data LIKE.TG ensures zero data loss, always keeping your data intact. It lets you monitor your workflow and stay in control with enhanced visibility and reliability to identify and address issues before they escalate. LIKE.TG provides you with 24/7 Customer Support to ensure you enjoy round-the-clock support when needed. With LIKE.TG , you have a reliable tool that lets you worry less about the data integration and helps you focus more on your business. Check LIKE.TG ’s in-depth documentation to learn more. Pricing at LIKE.TG Data LIKE.TG offers you with three simple and transparent pricing models, starting with the free plan which lets you ingest up to 1 million records. The Best-Suited Use Case for LIKE.TG Data If you are looking for advanced capabilities in automated data mapping and efficient change data capture, LIKE.TG is the best choice. LIKE.TG has great coverage, they keep their integrations fresh, and the tool is super reliable and accessible. The team was very responsive as well, always ready to answer questions and fix issues. It’s been a great experience! – Prudhvi Vasa, Head of Data, Postman Experience LIKE.TG : A Top Data Integration Tool for 2024 Feeling overwhelmed by the ever-growing list of data integration tools? Look no further! While other options may seem complex or limited, LIKE.TG offers a powerful and user-friendly solution for all your data needs. Get Started with LIKE.TG for Free 2. Dell Boomi Dell provides a cloud-based integration tool called Dell Boomi, this tool empowers your business to effortlessly integrate between applications, partners and customers through an intuitive visual designer and a wide array of pre-configured components. Boomi simplifies and supports ongoing integration and development task between multiple endpoints, irrespective of your organization’s size. Key Features of Dell Boomi Whether you’re an SMB or a large company, you can use this tool to support several application integrations as a service. With Dell Boomi, you can access a variety of integration and data management capabilities, including private-cloud, on-premise, and public-cloud endpoint connectors and robust ETL support. The tool allows your business to manage Data Integration in a central place via a unified reporting portal. Pricing at Dell Boomi Whether you’re an SMB or an Enterprise, Boomi offers you with easily understandable, flexible, and transparent pricing starting with basic features and ranging to advanced requirements. The Best-Suited Use Case for Dell Boomi Dell Boomi is a wise choice for managing and moving your data through hybrid IT architectures. 3. Informatica PowerCenter Informatica is a software development company that specializes in Data Integration. It provides ETL, data masking, data quality, data replication, data virtualization, master data management, and other services. You can connect it to and fetch data from a variety of heterogeneous sources and perform data processing. Key Features of Informatica PowerCenter You can manage and monitor your data pipelines with ease quickly identify and address any issues that might arise. You can ensure high data quality and accuracy using data cleansing, profiling, and standardization. It runs alongside an extensive catalog of related products for big data integration, cloud application integration, master data management, data cleansing, and other data management functions. Pricing at Informatica PowerCenter Informatica offers flexible, consumption-based pricing model enabling you to pay for what you need. For further information, you can contact their sales team. The Best-Suited Use Case for Informatica PowerCenter Powercenter is a good choice if you have to deal with many legacy data sources that are primarily on-premise. 4. Talend Talend is an ETL solution that includes data quality, application integration, data management, data integration, data preparation, and big data, among other features. Talend, after retiring its open-source version of Talend Studio, has joined hands with Qlik to provide free and paid versions of its data integration platform. They are committed to delivering updates, fixes, and vulnerability patches to ensure the platform remains secure and up-to-date. Key Features of Talend Talend also offers a wide array of services for advanced Data Integration, Management, Quality, and more. However, we are specifically referring to Talend Open Studio here. Your business can install and build a setup for both on-premise and cloud ETL jobs using Spark, Hadoop, and NoSQL Databases. To prepare data, your real-time team collaborations are permitted. Pricing at Talend Talend provides you with ready-to-query schemas, and advanced connectivity to improve data security included in its basic plan starting at $100/month. The Best-Suited Use Case for Talend If you can compromise on real-time data availability to save on costs, consider an open-source batch data migration tool like Talend. 5. Pentaho Pentaho Data Integration (PDI) provides you with ETL capabilities for obtaining, cleaning, and storing data in a uniform and consistent format. This tool is extremely popular and has established itself as the most widely used and desired Data Integration component. Key Features of Pentaho Pentaho Data Integration (PDI) is known for its simple learning curve and simplicity of usage. You can use Pentaho for multiple use cases that it supports outside of ETL in a Data Warehouse, such as database replication, database to flat files, and more. Pentaho allows you to create ETL jobs on a graphical interface without writing code. Pricing at Pentaho Pentaho has a free, open-source version and a subscription-based enterprise model. You can contact the sales team to learn the details about the subscription-based model. The Best-Suited Use Case for Pentaho Since PDI is open-source, it’s a great choice if you’re cost-sensitive. Pentaho, as a batch data integration tool, doesn’t support real-time data streaming. 6. AWS Glue AWS Glue is a robust data integration solution that excels in fully managed, cloud-based ETL processes on the Amazon Web Services (AWS) platform. Designed to help you discover, prepare, and combine data, AWS Glue simplifies analytics and machine learning. Key Features of the AWS Glue You don’t have to write the code for creating and running ETL jobs, this can be done simply by using AWS Glue Studio. Using AWS Glue, you can execute serverless ETL jobs. Also, other AWS services like S3, RDS, and Redshift can be integrated easily. Your data sources can be crawled and catalogued automatically using AWS Glue. Pricing at AWS Glue For AWS Glue the pay you make is hourly and the billing is done every second. You can request them for pricing quote. The Best-Suited Use Case for AWS Glue AWS Glue is a good choice if you’re looking for a fully managed, scalable and reliable tool involving cloud-based data integrations. 7. Microsoft Azure Data Factory Azure Data Factory is a cloud-based ETL and data integration service that allows you to create powerful workflows for moving and transforming data at scale. With Azure Data Factory, you can easily build and schedule data-driven workflows, known as pipelines, to gather data from various sources. Key Features of the Microsoft Azure Data Factory Data Factory offers a versatile integration and transformation platform that seamlessly supports and speeds up your digital transformation project using intuitive, code-free data flows. Using built-in connectors, you can ingest all your data from diverse and multiple sources. SQL Server Integration Services (SSIS) can be easily rehosted to build code-free ETL and ELT pipelines with built-in Git, supporting continuous integration and continuous delivery (CI/CD). Pricing at Microsoft Azure Data Factory Azure provides a consumption based pricing model, you can estimate your specific cost by using Azure Pricing Calculator available on the its website. The Best-Suited Use Case for the Microsoft Azure Data Factory Azure Data Factory is designed to automate and coordinate your data workflows across different sources and destinations. 8. IBM Infosphere Data Stage IBM DataStage is an enterprise-level data integration tool used to streamline your data transfer and transformation tasks. Data integration using ETL and ELT methods, along with parallel processing and load balancing is supported ensuring high performance. Key Features of IBM Infosphere Data Stage To integrate your structured, unstructured, and semi-structured data, you can use Data Stage. The platform provides a range of data quality features for you, including data profiling, standardization, matching, enhancement, and real-time data quality monitoring. By transforming large volumes of raw data, you can extract high-quality, usable information and ensure consistent and assimilated data for efficient data integrations. Pricing at IBM Infosphere Data Stage Data Stage offers free trial and there after you can contact their sales team to obtain the pricing for license and full version. The Best-Suited Use Case for IBM Infosphere Data Stage IBM Infosphere DataStage is recommended for you as the right integration tool because of its parallel processing capabilities it can handle large-scale data integrations efficiently along with enhancing performance. 9. SnapLogic SnapLogic is an integration platform as a service (iPaaS) that offers fast integration services for your enterprise. It comes with a simple, easy-to-use browser-based interface and 500+ pre-built connectors. With the help of SnapLogic’s Artificial Intelligence-based assistant, a person like you from any line of business can effortlessly integrate the two platforms using the click-and-go feature. Key Features of SnapLogic SnapLogic offers reporting tools that allow you to view the ETL job progress with the help of graphs and charts. It provides the simplest user interface, enabling you to have self-service integration. Anyone with no technical knowledge can integrate the source with the destination. SnapLogic’s intelligent system detects any EDI error, instantly notifies you, and prepares a log report for the issue. Pricing at SnapLogic SnapLogics’s pricing is based on the package you select and the configuration that you want with unlimited data flow. You can discuss the pricing package with their team. The Best-Suited Use Case for SnapLogic SnapLogic is an easy-to-use data integration tool that is best suited for citizen integrators without technical knowledge. 10. Jitterbit Jitterbit is a harmony integration tool that enables your enterprise to establish API connections between apps and services. It supports cloud-based, on-premise, and SaaS applications. Along with Data Integration tools, you are offered AI features that include speech recognition, real-time language translation, and a recommendation system. It is called the Swiss Army Knife of Big Data Integration Platforms. Key Features of Jitterbit Jitterbit offers a powerful Workflow Designer that allows you to create new integration between two apps with its pre-built data integration tool templates. It comes with an Automapper that can help you map similar fields and over 300 formulas to make the transformation task easier. Jitterbit provides a virtual environment where you can test integrations without disrupting existing ones. Pricing at Jitterbit Jitterbit offers you with three pricing models: Standard, Professional and Enterprise, all need an yearly subscription, and the quote can be discussed with them. The Best-Suited Use Case for Jitterbit Jitterbit is an Enterprise Integration Platform as a Service (EiPaaS) that you can use to solve complex integrations quickly. 11. Zigiwave Zigiwave is a Data Integration Tool for ITSM, Monitoring, DevOps, Cloud, and CRM systems. It can automate your workflow in a matter of few clicks as it offers a No-code interface for easy-to-go integrations. With its deep integration features, you can map entities at any level. Zigiwave smart data loss prevention protects data during system downtime. Key Features of Zigiwave Zigiwave acts as an intermediate between your two platforms and doesn’t store any data, which makes it a secure cloud Data Integration platform. Zigiwave synchronizes your data in real-time, making it a zero-lag data integration tool for enterprises. It is highly flexible and customizable and you can filter and map data according to your needs. Pricing at Zigiwave You can get a 30-day free trial at Zigiwave and can book a meeting with them to discuss the pricing. The Best-Suited Use Case for Zigiwave It is best suited if your company has fewer resources and wants to automate operations with cost-effective solutions. 12. IRI Voracity IRI Voracity is an iPaaS Data Integration tool that can connect your two apps with its powerful APIs. It also offers federation, masking, data quality, and MDM integrations. Its GUI workspace is designed on Eclipse to perform integrations, transformations, and Hadoop jobs. It offers other tools that help you understand and track data transfers easily. Key Features of IRI Voracity IRI Voracity generates detailed reports for ETL jobs that help you track all the activities and log all the errors. It also enables you to directly integrate their data with other Business Analytics and Business Intelligence tools to help analyze your data in one place. You can transform, normalize, or denormalize your data with the help of a GUI wizard. Pricing at IRI Voracity IRI Voracity offers you their pricing by asking for a quote. The Best-Suited Use Case for IRI Voracity If you’re familiar with Eclipse-based wizards and need the additional features of IRI Voracity Data Management, IRI Voracity, an Eclipse GUI-based data integration platform, is ideal for you. 13. Oracle Data Integrator Oracle Data Integrator is one of the most renowned Data Integration providers, offering seamless data integration for SaaS and SOA-enabled data services. It also offers easy interoperability with Oracle Warehouse Builder (OWB) for enterprise users like yourself. Oracle Data Integrator provides GUI-based tools for a faster and better user experience. Key Features of Oracle Data Integrator It automatically detects faulty data during your data loading and transforming process and recycles it before loading it again. It supports all RDBMSs, such as Oracle, Exadata, Teradata, IBM DB2, Netezza, Sybase IQ, and other file technologies, such as XML and ERPs. Its unique ETL architecture offers you greater productivity with low maintenance and higher performance for data transformation. Pricing at Oracle Data Integrator Though it is a free Open-Source platform, you can get Oracle Data Integrator Enterprise Editions Licence at $900 for a named user plus licence with $198 for software update registration support, and $30,000 for Processor Licence with $6,600 for software update licence support. The Best-Suited Use Case for Oracle Data Integrator The unique ETL architecture of Oracle Data Integrator eliminates the dedicated ETL servers, which reduces its hardware and software maintenance costs. So it’s best for your business if you want cost-effective data integration technologies. 14. Celigo Celigo is an iPaaS Data Integration tool with a click-and-go feature. It automates most of your workflow for data extraction and transformation to destinations. It offers many pre-built connectors, including most Cloud platforms used in the industry daily. Its user-friendly interface enables technical and non-technical users to perform data integration jobs within minutes. Key Features of Celigo Celigo offers a low-code GUI-based Flow Builder that allows you to build custom integrations from scratch. It provides an Autopilot feature with inegrator.io that allows you to automate most workflow with the help of pattern recognition AI. Using Celigo, developers like you can create and share your stacks and generate tokens for direct API calls for complex flow logic to build integrations. Pricing at Celigo Celigo offers four pricing plans: Free trail plan with 2 endpoint apps, Professional with 5 endpoint apps, Premium with 10 endpoint apps and Enterprise with 20 endpoint apps. Their prices can be known by contacting them. The Best-Suited Use Case for Celigo It is perfect if you want to automate most of your data integration workflow and have no coding knowledge. 15. MuleSoft Anypoint Platform MuleSoft Anypoint Platform is a unified iPaaS Data Integration tool that helps your company establish a connection between two cloud-based apps or a cloud or on-premise system for seamless data synchronization. It stores the data stream from data sources locally and on the Cloud. To access and transform your data, you can use the MuleSoft expression language. Key Features of the MuleSoft Anypoint Platform It offers mobile support that allows you to manage your workflow and monitor tasks from backend systems, legacy systems, and SaaS applications. MuleSoft can integrate with many enterprise solutions and IoT devices such as sensors, medical devices, etc. It allows you to perform complex integrations with pre-built templates and out-of-box connectors to accelerate the entire data transfer process. Pricing at MuleSoft Anypoint Platform Anypoint Integration Starter is the starting plan which lets you manage, design and deploy APIs and migrations and you can get the quote at request. The Best-Suited Use Case for the MuleSoft Anypoint Platform When your company needs to connect to many information sources, in public and private clouds and wants to access outdated system data, this integrated data platform is the best solution. What Factors to Consider While Selecting Data Integration Tools? While picking the right Data Integration tool from several great options out there, it is important to be wise enough. So, how would you select the best data integration platform for your use case? Here are some factors to keep in mind: Data Sources Supported Scalability Security and Compliance Real-Time Data Availability Data Transformations 1) Data Sources Supported As your business grows, the complexity of the Data Integration strategy will grow. Take note that there are many streams and web-based applications, and data sources that are being added to your business suit daily by different teams. Hence, it is important to choose a tool that could grow and can accommodate your expanding list of data sources as well. 2) Scalability Initially, the volume of the data you need for your Data Integration software could be less. But, as your business scales, you will start capturing every touchpoint of your customers, exponentially growing the volume of data that your data infrastructure should be capable of handling. When you choose your Data Integration tool, ensure that the tool can easily scale up and down as per your data needs. 3) Security and Compliance Given you are dealing with mission-critical data, you have to make sure that the solution offers the expertise and the resources needed to ensure that you are covered when it comes to security and compliance. 4) Real-Time Data Availability This is applicable only if you are use case is to bring data to your destination for real-time analysis. For many companies – this is the primary use case. Not all Data Integration solutions support this. Many bring data to the destination in batches – creating a lag of anywhere between a few hours to days. 5) Data Transformations The data that is extracted from different applications is in different formats. For example, the date represented in your database can be in epoch time whereas another system has the date in “mm-dd-yy”. To be able to do meaningful analysis, companies would want to bring data to the destination in a common format that makes analysis easy and fast. This is where Data transformation comes into play. Depending on your use case, pick a tool that enables seamless data transformations. Benefits of Data Integration Tools Now that you have your right tool based on your use case, it is time to learn how are they beneficial for your business. The benefits range from: Improved Decision-Making Since the raw data is now converted into usable information and data is present in a consolidated form, your decisions based on that information will be faster and more accurate. Automated Business Processes Using these tools your data integration task becomes automated, which leaves you and your team with more time to focus on business development related activities. Reduced Costs By utilizing these tools the integration processes are automated, so, manual efforts and errors are significantly reduced, therefore reducing the overall cost. Improved Customer Service You deliver more personalized customer support and it becomes efficient as you can now have a comprehensive customer report which will help you understand their needs. Enhanced Compliance and Security These tools make sure that the data handled follows proper regulatory standards and any of your sensitive information is protected. Increased Agility and Collaboration You can easily share your data and collaborate across departments without any interruptions which boosts the datas overall agility and responsiveness. Learn more about: Top 7 Free Open-source ETL Tools AWS Integration Strategies Conclusion This article provided you with a brief overview of Data Integration and Data Integration Tools, along with the factors to consider while choosing these tools. You are now in the position to choose the best Data Integration tools based on your requirements. Now that you have an idea of how to go about picking a Data Integration Tool, let us know your thoughts/questions in the comments section below. FAQ on Data Integration Tools What are the main features to look for in a data integration tool? The main features to look for in a data integration tool are the data sources it supports, its scalability, the security and compliance it follows, real-time data availability, and last but not the least, the data transformations it provides. How do data integration tools enhance data security? The data integration tools enhance data security by following proper regulatory standards and protecting your sensitive information. Can data integration tools handle real-time data? Integration tools like LIKE.TG Data, Talend, Jitterbit, and Zigiwave can handle real-time data. What are the cost considerations for different data integration tools? Cost consideration for different data integration tools include your initial licensing and subscription fees, along with the cost to implement and setup that tool followed by maintenance and support. How do I choose between open-source and proprietary tools? While choosing between open-source and proprietary tools you consider relevant factors, such as business size, scalability, available budget, deployment time and reputation of the data integration solution partner.

While Salesforce provides its analytics capabilities, many organizations need to synchronize Salesforce data into external databases like MySQL for consolidated analysis. This article explores two key methods for integrating Salesforce to MySQL: ETL pipeline and Custome Code. Read on for an overview of both integration methods and guidance on choosing the right approach.Methods to Set up Salesforce to MySQL Integration Method 1: Using LIKE.TG Data to Set Up Salesforce to MySQL Integration LIKE.TG Data, a No-code Data Pipeline platform helps you to transfer data from Salesforce (among 150+ Sources) to your desired destination like MySQL in real-time, in an effortless manner, and for free. LIKE.TG with its minimal learning curve can be set up in a matter of minutes making the user ready to perform operations in no time instead of making them repeatedly write the code. Sign up here for a 14-day Free Trial! Method 2: Using Custom Code to Set Up Salesforce to MySQL Integration You can follow the step-by-step guide for connecting Salesforce to MySQL using custom codes. This approach uses Salesforce APIs to achieve this data transfer. Additionally, it will also highlight the limitations and challenges of this approach. Methods to Set up Salesforce to MySQL Integration You can easily connect your Salesforce account to your My SQL account using the following 2 methods: Method 1: Using LIKE.TG Data to Set Up Salesforce to MySQL Integration LIKE.TG Data takes care of all your data preprocessing needs and lets you focus on key business activities and draw a much more powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent reliable solution to manage data in real-time and always has analysis-ready data in your desired destination. LIKE.TG can integrate data from Salesforce to MySQL in just 2 simple steps: Authenticate and configure your Salesforce data source as shown in the below image. To learn more about this step, visit here. Configure your MySQL destination where the data needs to be loaded, as shown in the below image. To learn more about this step, visit here. Method 2: Using Custom Code to Set Up Salesforce to MySQL Integration This method requires you to manually build a custom code using various Salesforce APIs to connect Salesforce to MySQL database. It is important to understand these APIs before learning the required steps. APIs Required to Connect Salesforce to MySQL Using Custom Code Salesforce provides different types of APIs and utilities to query the data available in the form of Salesforce objects. These APIs help to interact with Salesforce data. An overview of these APIs is as follows: Salesforce Rest APIs: Salesforce REST APIs provide a simple and convenient set of web services to interact with Salesforce objects. These APIs are recommended for implementing mobile and web applications that work with Salesforce objects. Salesforce REST APIs: Salesforce SOAP APIs are to be used when the applications need a stateful API or have strict requirements on transactional reliability. It allows you to establish formal contracts of API behavior through the use of WSDL. Salesforce BULK APIs: Salesforce BULK APIs are tailor-made for handling a large amount of data and have the ability to download Salesforce data as CSV files. It can handle data ranging from a few thousand records to millions of records. It works asynchronously and is batched. Background operation is also possible with Bulk APIs. Salesforce Data Loader: Salesforce also provides a Data Loader utility with export functionality. Data Loader is capable of selecting required attributes from objects and then exporting them to a CSV file. It comes with some limitations based on the Salesforce subscription plan to which the user belongs. Internally, Data Loader works based on bulk APIs. Steps to Connect Salesforce to MySQL Use the following steps to achieve Salesforce to MySQL integration: Step 1: Log in to Salesforce using the SOAP API and get the session id. For logging in first create an XML file named login.txt in the below format. <?xml version="1.0" encoding="utf-8" ?> <env:Envelope xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:env="http://schemas.xmlsoap.org/soap/envelope/"> <env:Body> <n1:login xmlns:n1="urn:partner.soap.sforce.com"> <n1:username>your_username</n1:username> <n1:password>your_password</n1:password> </n1:login> </env:Body> </env:Envelope> Step 2: Execute the below command to login curl https://login.Salesforce.com/services/Soap/u/47.0 -H "Content-Type: text/xml; charset=UTF-8" -H "SOAPAction: login" -d @login.txt From the resultant XML, note the session id. This session id is to be used for all subsequent requests. Step 3: Create a BULK API job. For doing this, create a text file in the folder named job.txt with the following content. <?xml version="1.0" encoding="UTF-8"?> <jobInfo xmlns="http://www.force.com/2009/06/asyncapi/dataload"> <operation>insert</operation> <object>Contact</object> <contentType>CSV</contentType> </jobInfo> Please note that the object attribute in the above XML should correspond to the object for which data is to be loaded. Here we are pulling data from the object called Contact. Execute the below command after creating the job.txt curl https://instance.Salesforce.com/services/async/47.0/job -H "X-SFDC-Session: sessionId" -H "Content-Type: application/xml; charset=UTF-8" -d @job.txt From the result, note the job id. This job-id will be used to form the URL for subsequent requests. Please note the URL will change according to the URL of the user’s Salesforce organization. Step 4: Use CURL again to execute the SQL query and retrieve results. curl https://instance_name—api.Salesforce.com/services/async/APIversion/job/jobid/batch -H "X-SFDC-Session: sessionId" -H "Content-Type: text/csv; SELECT name,desc from Contact Step 5: Close the job. For doing this, create a file called close.txt with the below entry. <?xml version="1.0" encoding="UTF-8"?> <jobInfo xmlns="http://www.force.com/2009/06/asyncapi/dataload"> <state>Closed</state> </jobInfo> Execute the below command after creating the file to close the job. curl https://instance.Salesforce.com/services/async/47.0/job/jobId -H "X-SFDC-Session: sessionId" -H "Content-Type: application/xml; charset=UTF-8" -d @close_job.txt Step 6: Retrieve the results id for accessing the URL for results. Execute the below command. curl -H "X-SFDC-Session: sessionId" https://instance.Salesforce.com/services/async/47.0/job/jobId/batch/batchId/result Step 7: Retrieve the actual results using the result ID fetched from the above step. curl -H "X-SFDC-Session: sessionId" https://instance.Salesforce.com/services/async/47.0/job/jobId/batch/batchId/result/resultId This will provide a CSV file with rows of data. Save the CSV file as contacts.csv. Step 8: Load data to MySQL using the LOAD DATA INFILE command. Assuming the table is already created this can be done by executing the below command. LOAD DATA INFILE'contacts.csv' INTO TABLE contacts FIELDS TERMINATED BY ',' ENCLOSED BY '"' LINES TERMINATED BY 'rn' IGNORE 1 LINES; Alternately, instead of using the bulk API manually, the Salesforce Data Loader utility can be used to export CSV files of objects. The caveat here is that usage of certain Data Loader functionalities is restricted based on the user’s subscription plan. There is also a limit to the frequency in which data loader export operations can be performed or scheduled. Limitations of Using Custom Code Method As evident from the above steps, loading data from Salesforce to MySQL through the manual method is both a tedious and fragile process with multiple error-prone steps. This works well when you have on-time or a batch need to bring data from Salesforce. In case you need data more frequently or in real-time, you would need to build additional processes to successfully achieve this. Conclusion In this blog, we discussed how to achieve Salesforce to MySQL Integration using 2 different approaches. Additionally, it has also highlighted the limitations and challenges of using the custom code method. Visit our Website to Explore LIKE.TG A more graceful method to achieve the same outcome would be to use a code-free Data Integration Platform likeLIKE.TG Data. LIKE.TG can mask all the ETL complexities and ensure that your data is securely moved to MySQL from Salesforce in just a few minutes and for free. Want to give LIKE.TG a spin? Sign Up for a 14-day free trialand experience the feature-rich LIKE.TG suite firsthand. Check out our pricing to choose the right plan for you! Let us know your thoughts on the 2 approaches to moving data from Salesforce to MySQL in the comments.

Often businesses have a different Database to store transactions (Eg: Amazon Aurora) and another Data Warehouse (Eg. Snowflake) for the company’s Analytical needs. There are 2 prime reasons to move data from your transactional Database to a Warehouse (Eg: Aurora to Snowflake). Firstly, the transaction Database is optimized for fast writes and responses. Running Analytics queries on large data sets with many aggregations and Joins will slow down the Database. This might eventually take a toll on the customer experience. Secondly, Data Warehouses are built to handle scaling data sets and Analytical queries. Moreover, they can host the data from multiple data sources and aid in deeper analysis. This post will introduce you to Aurora and Snowflake. It will also highlight the steps to move data from Aurora to Snowflake. In addition, you will explore some of the limitations associated with this method. You will be introduced to an easier alternative to solve these challenges. So, read along to gain insights and understand how to migrate data from Aurora to Snowflake. Understanding Aurora and Snowflake AWS RDS (Relational Database) is the initial Relation Database service from AWS which supports most of the open-source and proprietary databases. Open-source offerings of RDS like MySQL and PostgreSQL are much cost-effective compared to enterprise Database solutions like Oracle. But most of the time open-source solutions require a lot of performance tuning to get par with enterprise RDBMS in performance and other aspects like concurrent connections. AWS introduced a new Relational Database service called Aurora which is compatible with MySQL and PostgreSQL to overcome the much-known weakness of those databases costing much lesser than enterprise Databases. No wonder many organizations are moving to Aurora as their primary transaction Database system. On the other end, Snowflake might be the best cost-effective and fast Data Warehousing solution. It has dynamically scaling compute resources and storage is completely separated and billed. Snowflake can be run on different Cloud vendors including AWS. So data movement from Aurora to Snowflake can also be done with less cost. Read about Snowflake’s features here. Methods to load data from Amazon Aurora to Snowflake Here are two ways that can be used to approach Aurora to Snowflake ETL: Method 1:Build Custom Scripts to move data from Aurora to Snowflake Method 2:Implement a hassle-free, no-code Data Integration Platform like LIKE.TG Data –14 Day Free Trial(Official Snowflake ETL Partner) to move data from Aurora to Snowflake. GET STARTED WITH LIKE.TG FOR FREE This post will discuss Method 1 in detail to migrate data from Aurora to Snowflake. The blog will also highlight the limitations of this approach and the workarounds to solve them. Move Data from Aurora to Snowflake using ETL Scripts The steps to replicate data from Amazon Aurora to Snowflake are as follows: 1. Extract Data from Aurora Cluster to S3 SELECT INTO OUTFILE S3 statement can be used to query data from an Aurora MySQL cluster and save the result to S3. In this method, data reaches the client-side in a fast and efficient manner. To save data to S3 from an Aurora cluster proper permissions need to be set. For that – Create a proper IAM policy to access S3 objects – Refer to AWS documentation here. Create a new IAM role, and attach the IAM policy you created in the above step. Set aurora_select_into_s3_role or aws_default_s3_role cluster parameter to the ARN of the new IAM role. Associate the IAM role that you created with the Aurora cluster. Configure the Aurora cluster to allow outbound connections to S3 – Read more on this here. Other important points to be noted while exporting data to S3: User Privilege – The user that issues the SELECT INTO OUTFILE S3 should have the privilege to do so.To grant access – GRANT SELECT INTO S3 ON *.* TO 'user'@'domain'. Note that this privilege is specific to Aurora. RDS doesn’t have such a privilege option. Manifest File – You can set the MANIFEST ON option to create a manifest file which is in JSON format that lists the output files uploaded to the S3 path. Note that files will be listed in the same order in which they would be created.Eg: { "entries": [ { "url":"s3-us-east-1://s3_bucket/file_prefix.part_00000" }, { "url":"s3-us-east-1://s3_bucket/file_prefix.part_00001" }, { "url":"s3-us-east-1://s3_bucket/file_prefix.part_00002" } ] } Output Files – The output is stored as delimited text files. As of now compressed or encrypted files are not supported. Overwrite Existing File – Set option OVERWRITE ON to delete if a file with exact name exists in S3. The default file size is 6 GB. If the data selected by the statement is lesser then a single file is created. Otherwise, multiple files are created. No rows will be split across file boundaries. If the data volume to be exported is larger than 25 GB, it is recommended to run multiple statements to export data. Each statement for a different portion of data. No metadata like table schema will be uploaded to S3 As of now, there is no direct way to monitor the progress of data export. One simple method is set to manifest option on and the manifest file will be the last file created.Examples: The below statement writes to S3 of located in a different region. Each field is terminated by a comma and each row is terminated by ‘n’. SELECT * FROM students INTO OUTFILE S3 's3-us-west-2://aurora-out/sample_students_data' FIELDS TERMINATED BY ',' LINES TERMINATED BY 'n'; Below is another example that writes to S3 of located in the same region. A manifest file will also be created. SELECT * FROM students INTO OUTFILE S3 's3://aurora-out/sample_students_data' FIELDS TERMINATED BY ',' LINES TERMINATED BY 'n' MANIFEST ON; 2. Convert Data Types and Format them There might be data transformations corresponding to business logic or organizational standards to be applied while transferring data from Aurora to Snowflake. Apart from those high-level mappings, some basic things to be considered generally are listed below: All popular character sets including UTF-8, UTF-16 are supported by Snowflake. The full list can be found here. Many Cloud-based and open source Big Data systems compromise on standard Relational Database constraints like Primary Key. But, note that Snowflake supports all SQL constraints like UNIQUE, PRIMARY KEY, FOREIGN KEY, NOT NULL constraints. This might be helpful when you load data. Data types support in Snowflake is fairly rich including nested data structures like an array. Below is the list of Snowflake data types and corresponding MySQL Aurora types. Snowflake is really flexible with the date or time format. If a custom format is used in your file that can be explicitly specified using the File Format Option while loading data to the table. The complete list of date and time formats can be found here. 3. Stage Data Files to the Snowflake Staging Area Snowflake requires the data to be uploaded to a temporary location before loading to the table. This temporary location is an S3 location that Snowflake has access to. This process is called staging. The snowflake stage can be either internal or external. (A) Internal Stage In Snowflake, each user and table is automatically assigned to an internal stage for data files. It is also possible internal stages explicitly and can be named. The stage assigned to the user is named as ‘@~’. The stage assigned to a table will have the name of the table. The default stages assigned to a user or table can’t be altered or dropped. The default stages assigned to a user or table do not support setting file format options. As mentioned above, internal stages can also be created explicitly by the user using SQL statements. While creating stages explicitly like this, many data loading options can be assigned to those stages like file format, date format, etc. While interacting with Snowflake for data loading or creating tables, SnowSQL is a very handy CLI client available in Linux/Mac/Windows which can be used to run Snowflake commands. Read more about the tool and options here. Below are some example commands to create a stage: Create a named internal stage as shown below: my_aurora_stage and assign some default options: create or replace stage my_aurora_stage copy_options = (on_error='skip_file') file_format = (type = 'CSV' field_delimiter = '|' skip_header = 1); PUT is the command used to stage files to an internal Snowflake stage. The syntax of the PUT command is : PUT file://path_to_file/filename internal_stage_name Eg: Upload a file named students_data.csv in the /tmp/aurora_data/data/ directory to an internal stage named aurora_stage. put file:////tmp/aurora_data/data/students_data.csv @aurora_stage; Snowflake provides many options which can be used to improve the performance of data load like the number of parallelisms while uploading the file, automatic compression, etc. More information and the complete list of options are listed here. (B) External Stage Just like the internal stage Snowflake supports Amazon S3 and Microsoft Azure as an external staging location. If data is already uploaded to an external stage that can be accessed from Snowflake, that data can be loaded directly to the Snowflake table. No need to move the data to an internal stage. To create an external stage on S3, IAM credentials with proper access permissions need to be provided. In case the data is encrypted, encryption keys should be provided. create or replace stage aurora_ext_stage url='s3://snowflake_aurora/data/load/files/' credentials=(aws_key_id='13311a23344rrb3c' aws_secret_key='abddfgrrcd4kx5y6z'); encryption=(master_key = 'eSxX0jzsdsdYfIjkahsdkjamNNNaaaDwOaO8='); Data can be uploaded to the external stage with respective Cloud services. Data from Amazon Aurora will be exported to S3 and that location itself can be used as an external staging location which helps to minimize data movement. 4. Import Staged Files to Snowflake Table Now data is present in an external or internal stage and has to be loaded to a Snowflake table. The command used to do this is COPY INTO. To execute the COPY INTO command compute resources in the form of Snowflake virtual warehouses are required and will be billed as per consumption. Eg: To load from a named internal stage: copy into aurora_table from @aurora_stage; To load data from the external stage. Only a single file is specified. copy into my_external_stage_table from @aurora_ext_stage/tutorials/dataloading/students_ext.csv; You can even copy directly from an external location: copy into aurora_table from s3://mybucket/aurora_snow/data/files credentials=(aws_key_id='$AWS_ACCESS_KEY_ID' aws_secret_key='$AWS_SECRET_ACCESS_KEY') encryption=(master_key = 'eSxX009jhh76jkIuLPH5r4BD09wOaO8=') file_format = (format_name = csv_format); Files can be specified using patterns: copy into aurora_pattern_table from @aurora_stage file_format = (type = 'TSV') pattern='.*/.*/.*[.]csv[.]gz'; Some commonly used options for CSV file loading using the COPY command COMPRESSION to specify compression algorithm used for the files RECORD_DELIMITER to indicate lines separator character FIELD_DELIMITER is the character separating fields in the file SKIP_HEADER is the number of header lines skipped DATE_FORMAT is the date format specifier TIME_FORMAT is the time format specifier There are many other options. For the full list click here. 5. Update Snowflake Table So far the blog talks about how to extract data from Aurora and simply insert it into a Snowflake table. Next, let’s look deeper into how to handle incremental data upload to the Snowflake table. Snowflake’s architecture is unique. It is not based on any current/existing big data framework. Snowflake does not have any limitations for row-level updates. This makes delta data uploading to a Snowflake table much easier compared to systems like Hive. The way forward is to load incrementally extracted data to an intermediate table. Next, as per the data in the intermediate table, modify the records in the final table. 3 common methods that are used to modify the final table once data is loaded into a landing table ( intermediate table) are mentioned below. 1. Update the rows in the target table. Next, insert new rows from the intermediate or landing table which are not in the final table. UPDATE aurora_target_table t SET t.value = s.value FROM landing_delta_table in WHERE t.id = in.id; INSERT INTO auroa_target_table (id, value) SELECT id, value FROM landing_delta_table WHERE NOT id IN (SELECT id FROM aurora_target_table); 2. Delete all records from the target table which are in the landing table. Then insert all rows from the landing table to the final table. DELETE .aurora_target_table f WHERE f.id IN (SELECT id from landing_table); INSERT aurora_target_table (id, value) SELECT id, value FROM landing_table; 3. MERGE statement – Inserts and updates combined in a single MERGE statement and it is used to apply changes in the landing table to the target table with one SQL statement. MERGE into aurora_target_table t1 using landing_delta_table t2 on t1.id = t2.id WHEN matched then update set value = t2.value WHEN not matched then INSERT (id, value) values (t2.id, t2.value); Limitations of Writing CustomETL Code to Move Data from Aurora to Snowflake While the approach may look very straightforward to migrate data from Aurora to Snowflake, it does come with limitations. Some of these are listed below: You would have to invest precious engineering resources to hand-code the pipeline. This will increase the time for the data to be available in Snowflake. You will have to invest in engineering resources to constantly monitor and maintain the infrastructure. Code Breaks, Schema Changes at the source, Destination Unavailability – these issues will crop up more often than you would account for while starting the ETL project. The above approach fails if you need data to be streamed in real-time from Aurora to Snowflake. You would need to add additional steps, set up cron jobs to achieve this. So, to overcome these limitations and to load your data seamlessly from Amazon Aurora to Snowflake you can use a third-party tool like LIKE.TG . EASY WAY TO MOVE DATA FROM AURORA TO SNOWFLAKE On the other hand, a Data Pipeline Platform such asLIKE.TG , an official Snowflake ETL partner,can help you bring data from Aurora to Snowflake in no time. Zero Code, Zero Setup Time, Zero Data Loss. Here are the simple steps to loaddata from Aurora to Snowflake using LIKE.TG : Authenticate and Connect to your Aurora DB. Select the replication mode: (a) Full Dump and Load (b) Incremental load for append-only data (c) Change Data Capture Configure the Snowflake Data Warehouse for data load. SIGN UP HERE FOR A 14-DAY FREE TRIAL! For a next-generation digital organization, there should be a seamless data movement between Transactional and Analytical systems. Using an intuitive and reliable platform like LIKE.TG to migrate your data from Aurora to Snowflake ensures that accurate and consistent data is available in Snowflake in real-time. Conclusion In this article, you gained a basic understanding of AWS Aurora and Snowflake. Moreover, you understood the steps to migrate your data from Aurora to Snowflake using Custom ETL scripts. In addition, you explored the limitations of this method. Hence, you were introduced to an easier alternative, LIKE.TG to move your data from Amazon Aurora to Snowflake seamlessly. VISIT OUR WEBSITE TO EXPLORE LIKE.TG LIKE.TG Data is a No-Code Data Pipeline that offers a faster way to move data from 150+ Data Sources including 50+ Free Sources, into your Data Warehouse like Amazon Redshift to be visualized in a BI tool. LIKE.TG is fully automated and hence does not require you to code. You can easily load your data from Aurora to Snowflake in a hassle-free manner. Want to take LIKE.TG for a spin? Check out our transparent pricing to make an informed decision. SIGN UP and experience a hassle-free data replication from Aurora to Snowflake. Share your experience of migrating data from Aurora to Snowflake in the comments section below!

Snowflake is great if you have big data needs. It offers scalable computing and limitless size in a traditional SQL and Data Warehouse setting. If you have a relatively small dataset or low concurrency/load then you won’t see the benefits of Snowflake.Simply put, Snowflake has a friendly UI, and unlimited storage capacity, along with the control, security, and performance you’d expect for a Data Warehouse, something SQL Server is not. Snowflake’s unique Cloud Architecture enables unlimited scale and concurrency without resource contention, the ‘Holy Grail’ of Data Warehousing. One of the biggest challenges of migrating data from SQL server to Snowflake is choosing from all the different options available. This blog post covers the detailed steps of 4 methods that you need to follow for SQL Server to Snowflake migration. Read along and decide, which method suits you the best! What is MS SQL Server? Microsoft SQL Server (MS SQL Server) is a relational database management system (RDBMS) developed by Microsoft. It is used to store and retrieve data as requested by other software applications, which may run either on the same computer or on another computer across a network. MS SQL Server is designed to handle a wide range of data management tasks and supports various transaction processing, business intelligence, and analytics applications. Key Features of SQL Server: Scalability: Supports huge databases and multiple concurrent users. High Availability: Features include Always On and Failover clustering. Security: Tight security through solid encryption, auditing, row-level security. Performance: High-Speed in-memory OLTP and Columnstore indexes Integration: Integrates very well with other Microsoft services and Third-Party Tools Data Tools: In-Depth tools for ETL, reporting, data analysis Cloud Integration: Comparatively much easier to integrate with Azure services Management: SQL Server Management Studio for the management of Databases Backup and Recovery: Automated Backups, Point-in-Time Restore. TSQL: Robust Transact-SQL in complex queries and stored procedures. What is Snowflake? Snowflake is a cloud-based data warehousing platform that is designed to handle large-scale data storage, processing, and analytics. It stands out due to its architecture, which separates compute, storage, and services, offering flexibility, scalability, and performance improvements over traditional data warehouses. Key Features of Snowflake: Scalability: Seamless scaling of storage and compute independently. Performance: Fast query performance with automatic optimization. Data Sharing: Secure and easy data sharing across organizations. Multi-Cloud: Operates on AWS, Azure, and Google Cloud. Security: Comprehensive security features including encryption and role-based access. Zero Maintenance: Fully managed with automatic updates and maintenance. Data Integration: Supports diverse data formats and ETL tools. Load your data from MS SQL Server to SnowflakeGet a DemoTry itLoad your data from Salesforce to SnowflakeGet a DemoTry itLoad your data from MongoDB to SnowflakeGet a DemoTry it Methods to Connect SQL Server to Snowflake The following 4 methods can be used to transfer data from Microsoft SQL server to Snowflake easily: Method 1: Using SnowSQL to connect SQL server to Snowflake Method 2: Using Custom ETL Scripts to connect SQL Server to Snowflake Method 3: Using LIKE.TG Data to connect Microsoft SQL Server to Snowflake Method 4: SQL Server to Snowflake Using Snowpipe Method 1: Using SnowSQL to Connect Microsoft SQL Server to Snowflake To migrate data from Microsoft SQL Server to Snowflake, you must perform the following steps: Step 1: Export data from SQL server using SQL Server Management Studio Step 2: Upload the CSV file to an Amazon S3 Bucket using the web console Step 3: Upload data to Snowflake From S3 Step 1: Export Data from SQL Server Using SQL Server Management Studio SQL Server Management Studio is a data management and administration software application that launched with SQL Server. You will use it to extract data from a SQL database and export it to CSV format. The steps to achieve this are: Install SQL Server Management Studio if you don’t have it on your local machine. Launch the SQL Server Management Studio and connect to your SQL Server. From the Object Explorer window, select the database you want to export and right-click on the context menu in the Tasks sub-menu and choose the Export data option to export table data in CSV. The SQL Server Import and Export Wizard welcome window will pop up. At this point, you need to select the Data source you want to copy from the drop-down menu. After that, you need to select SQL Server Native Client 11.0 as the data source. Select an SQL Server instance from the drop-down input box. Under Authentication, select “Use Windows Authentication”. Just below that, you get a Database drop-down box, and from here you select the database from which data will be copied. Once you’re done filling out all the inputs, click on the Next button. The next window is the Choose a Destination window. Under the destination drop-down box, select the Flat File Destination for copying data from SQL Server to CSV. Under File name, select the CSV file that you want to write to and click on the Next button. In the next screen, select Copy data from one or more tables or views and click Next to proceed. A “Configure Flat File Destination” screen will appear, and here you are going to select the table from the Source table or view. This action will export the data to the CSV file. Click Next to continue. You don’t want to change anything on the Save and Run Package window so just click Next. The next window is the Complete Wizard window which shows a list of choices that you have selected during the exporting process. Counter-check everything and if everything checks out, click the Finish button to begin exporting your SQL database to CSV. The final window shows you whether the exporting process was successful or not. If the exporting process is finished successfully, you will see a similar output to what’s shown below. Step 2: Upload the CSV File to an Amazon S3 Bucket Using the Web Console After completing the exporting process to your local machine, the next step in the data transfer process from SQL Server to Snowflake is to transfer the CSV file to Amazon S3. Steps to upload a CSV file to Amazon S3: Start by creating a storage bucket. Go to the AWS S3 Console Click the Create Bucket button and enter a unique name for your bucket on the form. Choose the AWS Region where you’d like to store your data. Create a new S3 bucket. Create the directory that will hold your CSV file. In the Buckets pane, click on the name of the bucket that you created. Click on the Actions button, and select the Create Folder option. Enter a unique name for your new folder and click Create. Upload the CSV file to your S3 bucket. Select the folder you’ve just created in the previous step. Select Files wizard and then click on the Add Files button in the upload section. Next, a file selection dialog box will open. Here you will select the CSV file you exported earlier and then click Open. Click on the Start Upload button and you are done! Move your SQL Server Data to Snowflake using LIKE.TG Start for Free Now Step 3: Upload Data to Snowflake From S3 Since you already have an Amazon Web Services (AWS) account and you are storing your data files in an S3 bucket, you can leverage your existing bucket and folder paths for bulk loading into Snowflake. To allow Snowflake to read data from and write data to an Amazon S3 bucket, you first need to configure a storage integration object to delegate authentication responsibility for external cloud storage to a Snowflake identity and access management (IAM) entity. Step 3.1: Define Read-Write Access Permissions for the AWS S3 Bucket Allow the following actions: “s3:PutObject” “s3:GetObject” “s3:GetObjectVersion” “s3:DeleteObject” “s3:DeleteObjectVersion” “s3:ListBucket” The following sample policy grants read-write access to objects in your S3 bucket. { "Version": "2012-10-17", "Statement": [ { "Sid": "AllowListingOfUserFolder", "Action": [ "s3:ListBucket", "s3:GetBucketLocation" ], "Effect": "Allow", "Resource": [ "arn:aws:s3:::bucket_name" ] }, { "Sid": "HomeDirObjectAccess", "Effect": "Allow", "Action": [ "s3:PutObject", "s3:GetObject", "s3:DeleteObjectVersion", "s3:DeleteObject", "s3:GetObjectVersion" ], "Resource": "arn:aws:s3:::bucket_name/*" } ] } For a detailed explanation of how to grant access to your S3 bucket, check out this link. Step 3.2: Create an AWS IAM Role and record your IAM Role ARN value located on the role summary page because we are going to need it later on. Step 3.3: Create a cloud storage integration using the STORAGE INTEGRATION command. CREATE STORAGE INTEGRATION <integration_name> TYPE = EXTERNAL_STAGE STORAGE_PROVIDER = S3 ENABLED = TRUE STORAGE_AWS_ROLE_ARN = '<iam_role>' STORAGE_ALLOWED_LOCATIONS = ('s3://<bucket>/<path>/', 's3://<bucket>/<path>/') [ STORAGE_BLOCKED_LOCATIONS = ('s3://<bucket>/<path>/', 's3://<bucket>/<path>/') ] Where: <integration_name> is the name of the new integration. <iam_role is> the Amazon Resource Name (ARN) of the role you just created. <bucket> is the name of an S3 bucket that stores your data files. <path> is an optional path that can be used to provide granular control over objects in the bucket. Step 3.4: Recover the AWS IAM User for your Snowflake Account Execute the DESCRIBE INTEGRATION command to retrieve the ARN for the AWS IAM user that was created automatically for your Snowflake account:DESC INTEGRATION <integration_name>; Record the following values: Step 3.5: Grant the IAM User Permissions to Access Bucket Objects Log into the AWS Management Console and from the console dashboard, select IAM. Navigate to the left-hand navigation pane and select Roles and choose your IAM Role. Select Trust Relationships followed by Edit Trust Relationship. Modify the policy document with the IAM_USER_ARNand STORAGE_AWS_EXTERNAL_ID output values you recorded in the previous step. { "Version": "2012-10-17", "Statement": [ { "Sid": "", "Effect": "Allow", "Principal": { "AWS": "<IAM_USER_ARN>" }, "Action": "sts:AssumeRole", "Condition": { "StringEquals": { "sts:ExternalId": "<STORAGE_AWS_EXTERNAL_ID>" } } } ] } Click the Update Trust Policy button to save the changes. Step 3.6: Create an External Stage that references the storage integration you created grant create stage on schema public to role <IAM_ROLE>; grant usage on integration s3_int to role <IAM_ROLE>; use schema mydb.public; create stage my_s3_stage storage_integration = s3_int url = 's3://bucket1/path1' file_format = my_csv_format; Step 3.7: Execute COPY INTO <table> SQL command to load data from your staged files into the target table using the Snowflake client, SnowSQL. Seeing that we have already configured an AWS IAM role with the required policies and permissions to access your external S3 bucket, we have already created an S3 stage. Now that we have a stage built in Snowflake pulling this data into your tables will be extremely simple. copy into mytable from s3://mybucket credentials=(aws_key_id='$AWS_ACCESS_KEY_ID' aws_secret_key='$AWS_SECRET_ACCESS_KEY') file_format = (type = csv field_delimiter = '|' skip_header = 1); This SQL command loads data from all files in the S3 bucket to your Snowflake Warehouse. SQL Server to Snowflake: Limitations and Challenges of Using Custom Code Method The above method of connecting SQL Server to Snowflake comes along with the following limitations: This method is only intended for files that do not exceed 160GB. Anything above that will require you to use the Amazon S3 REST API. This method doesn’t support real-time data streaming from SQL Server into your Snowflake DW. If your organization has a use case for Change Data Capture (CDC), then you could create a data pipeline using Snowpipe. Also, although this is one of the most popular methods of connecting SQL Server to Snowflake, there are a lot of steps that you need to get right to achieve a seamless migration. Some of you might even go as far as to consider this approach to be cumbersome and error-prone. Method 2: Using Custom ETL Scripts Custom ETL scripts are programs that extract, transform, and load data from SQL Server to Snowflake. They require coding skills and knowledge of both databases. To use custom ETL scripts, you need to: 1. Install the Snowflake ODBC driver or a client library for your language (e.g., Python, Java, etc.). 2. Get the connection details for Snowflake (e.g., account name, username, password, warehouse, database, schema, etc.). 3. Choose a language and set up the libraries to interact with SQL Server and Snowflake. 4. Write a SQL query to extract the data you want from SQL Server. Use this query in your script to pull the data. Drawbacks of Utilizing ETL Scripts While employing custom ETL scripts to transfer data from SQL Server to Snowflake offers advantages, it also presents potential drawbacks: Complexity and Maintenance Burden: Custom scripts demand more resources for development, testing, and upkeep compared to user-friendly ETL tools, particularly as data sources or requirements evolve. Limited Scalability: Custom scripts may struggle to efficiently handle large data volumes or intricate transformations, potentially resulting in performance challenges unlike specialized ETL tools. Security Risks: Managing credentials and sensitive data within scripts requires meticulous attention to security. Storing passwords directly within scripts can pose significant security vulnerabilities if not adequately safeguarded. Minimal Monitoring and Logging Capabilities: Custom scripts may lack advanced monitoring and logging features, necessitating additional development effort to establish comprehensive tracking mechanisms. Extended Development Duration: Developing custom scripts often takes longer compared to configuring ETL processes within visual tools. Method 3: Using LIKE.TG Data to Connect SQL Server to Snowflake 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), we help you not only export data from sources load data to the destinations but also transform enrich your data, make it analysis-ready. The following steps are required to connect Microsoft SQL Server to Snowflake using LIKE.TG ’s Data Pipeline: Step 1: Connect to your Microsoft SQL Server source. ClickPIPELINESin theNavigation Bar. Click+ CREATEin thePipelines List View. Select SQL Server as your source. In theConfigure yourSQL ServerSourcepage, specify the following: You can read more about using SQL server as a source connector for LIKE.TG here. Step 2: Configure your Snowflake Data Warehouse as Destination ClickDESTINATIONSin theNavigation Bar. Click+ CREATEin theDestinations List View. In theAdd Destinationpage, selectSnowflakeas the Destination type. In theConfigure yourSnowflakeWarehousepage, specify the following: This is how simple it can be to load data from SQL Server to Snowflake using LIKE.TG . Method 4: SQL Server to Snowflake Using Snowpipe Snowpipe is a feature of Snowflake that allows you to load data from external sources into Snowflake tables automatically and continuously. Here are the steps involved in this method: 1. Create an external stage in Snowflake that points to an S3 bucket where you will store the CSV file. 2. Create an external stage in Snowflake that points to an S3 bucket where you will store the CSV file. 3. Create a pipe in Snowflake that copies data from the external stage to the table. Enable auto-ingest and specify the file format as CSV. 4. Enable Snowpipe with the below command ALTER ACCOUNT SET PIPE_EXECUTION_PAUSED = FALSE; 5. Install the Snowpipe JDBC driver on your local machine and create a batch file to export data from SQL Server to CSV File. 6. Schedule the batch file to run regularly using a tool like Windows Task Scheduler or Cron. Check out this documentation for more details. Drawbacks of Snowpipe Method Here are some key limitations of using Snowpipe for data migration from SQL Server to Snowflake: File Size Restrictions: Snowflake imposes a per-file size limit for direct ingestion (around 160GB). Files exceeding this necessitate additional steps like splitting them or using the S3 REST API, adding complexity. Real-Time/CDC Challenges: Snowpipe is ideal for micro-batches and near real-time ingestion. But, it isn’t built for true real-time continuous data capture (CDC) of every single change happening in your SQL Server. Error Handling: Error handling for failed file loads through Snowpipe can become a bit nuanced. You need to configure options like ON_ERROR = CONTINUE in your COPY INTO statements to prevent individual file failures from stopping the entire load process. Transformation Limitations: Snowpipe primarily handles loading data into Snowflake. For complex transformations during the migration process, you may need a separate ETL/ELT tool to work with the Snowpipe-loaded data within Snowflake. Why migrate data from MS SQL Server to Snowflake? Enhanced Scalability and Elasticity: MSSQL Server, while scalable, often requires manual infrastructure provisioning for scaling compute resources. Snowflake’s cloud-based architecture offers elastic scaling, allowing you to easily adjust compute power up or down based on workload demands. You only pay for the resources you use, leading to potentially significant cost savings. Reduced Operational Burden: Managing and maintaining on-premises infrastructure associated with MSSQL Server can be resource-intensive. Snowflake handles all infrastructure management, freeing up your IT team to focus on core data initiatives. Performance and Concurrency: Snowflake’s architecture is designed to handle high concurrency and provide fast query performance, making it suitable for demanding analytical workloads and large-scale data processing. Additional Resources on SQL Server to Snowflake Explore more about Loading Data to Snowflake Conclusion The article introduced you to how to migrate data from SQL server to Snowflake. It also provided a step-by-step guide of 4 methods using which you can connect your Microsoft SQL Server to Snowflake easily. The article also talked about the limitations and benefits associated with these methods. The manual method using SnowSQL works fine when it comes to transferring data from Microsoft SQL Server to Snowflake, but there are still numerous limitations to it. FAQ on SQL Server to Snowflake Can you connect SQL Server to Snowflake? Connecting the SQL server to Snowflake is a straightforward process. You can do this using ODBC drivers or through automated platforms like LIKE.TG , making the task more manageable. How to migrate data from SQL to Snowflake? To migrate your data from SQL to Snowflake using the following methods:Method 1: Using SnowSQL to connect the SQL server to SnowflakeMethod 2: Using Custom ETL Scripts to connect SQL Server to SnowflakeMethod 3: Using LIKE.TG Data to connect Microsoft SQL Server to SnowflakeMethod 4: SQL Server to Snowflake Using Snowpipe Why move from SQL Server to Snowflake? We need to move from SQL Server to Snowflake because it provides:1. Enhanced scalability and elasticity.2. Reduced operational burden.3. High concurrency and fast query performance. Can SQL be used for snowflakes? Yes, snowflake provides a variant called Snowflake SQL which is ANSI SQL-compliant. What are your thoughts about the different approaches to moving data from Microsoft SQL Server to Snowflake? Let us know in the comments.

If you wish to move your data from DynamoDB to BigQuery, then you are on the right page. This post aims to help you understand the methods to move data from DynamoDB to BigQuery. But, before we get there, it is important to briefly understand the features of DynamoDB and BigQuery.Introduction to DynamoDB and Google BigQuery DynamoDB and BigQuery are popular, fully managed cloud databases provided by the two biggest names in Tech. Having launched for business in 2012 and 2010 respectively, these come as part of a host of services offered by their respective suite of services. This makes the typical user wanting to stick to just one, a decision that solidifies as one looks into the cumbersome process of setting up and maximizing the potential of having both these up and running parallelly. That being said, businesses still end up doing this for a variety of reasons, and therein lies the relevance of discussing this topic. Moving data from DynamoDB to BigQuery As mentioned before, because these services are offered by two different companies that want everything to be done within their tool suite, it is a non-trivial task to move data seamlessly from one to the other. Here are thetwo ways to move data from DynamoDB to BigQuery: 1) UsingLIKE.TG Data: An easy-to-use integration platform that gets the job done with minimal effort. 2) Using Custom Scripts: You can custom build your ETL pipeline by hand-coding scripts. This article aims to guide the ones that have opted to move data on their own from DynamoDB to BigQuery. The blog would be able to guide you with a step-by-step process, make you aware of the pitfalls and provide suggestions to overcome them. Steps to Move Data from DynamoDB to Bigquery using Custom Code Method Below are the broad steps that you would need to take to migrate your data from DynamoDB to BigQuery. Each of these steps is further detailed in the rest of the article. Step 1: Export the DynamoDB Data onto Amazon S3 Step 2: Setting Up Google Cloud Storage and Copy Data from Amazon S3 Step 3: Import the Google Cloud Storage File into the BigQuery Table Step 1: Export the DynamoDB Data onto Amazon S3 The very first step is to transfer the source DynamoDB data to Amazon S3. Both S3 and GCS(Google Cloud Storage) support CSV as well as JSON files but for demonstration purposes, let’s take the CSV example. The actual export from DynamoDB to S3 can be done using the Command Line or via the AWS Console. Method 1The command-line method is a two-step process. First, you export the table data into a CSV file: $aws dynamodb scan --table-name LIKE.TG _dynamo --output > LIKE.TG .txt The above would produce a tab-separated output file which can then be easily converted to a CSV file. This CSV file (LIKE.TG .csv, let’s say) could then be uploaded to an S3 bucket using the following command: $aws s3 cp LIKE.TG .csv s3://LIKE.TG bucket/LIKE.TG .csv Method 2If you prefer to use the console, sign in to your Amazon Console here. The steps to be followed on the console are mentioned in detail in the AWS documentation here. Step 2: Setting Up Google Cloud Storage and Copy Data from Amazon S3 The next step is to move the S3 data file onto Google Cloud Storage. As before, there is a command-line path as well as the GUI method to get this done. Let’s go through the former first. Using gsutilgsutil is a command-line service to access and do a number of things on Google Cloud; primarily it is used to work with the GCS buckets. To create a new bucket the following command could be used: $gsutil mb gs://LIKE.TG _gc/LIKE.TG You could mention a bunch of parameters in the above command to specify the cloud location, retention, etc. (full list here under ‘Options’) per your requirements. An interesting thing about BigQuery is that it generally loads uncompressed CSV files faster than compressed ones. Hence, unless you are sure of what you are doing, you probably shouldn’t run a compression utility like gzip on the CSV file for the next step. Another thing to keep in mind with GCS and your buckets is setting up access control. Here are all the details you will need on that. The next step is to copy the S3 file onto this newly created GCS bucket. The following copy command gets that job done: $gsutil cp s3://LIKE.TG _s3/LIKE.TG .csv/ gs://LIKE.TG _gc/LIKE.TG .csv BigQuery Data Transfer Service This is a relatively new and faster way to get the same thing done. Both CSV and JSON files are supported by this service however there are limitations that could be found here and here. Further documentation and the detailed steps on how to go about this can be found here. Step 3: Import the Google Cloud Storage File into the BigQuery Table Every BigQuery table lies in a specific data set of a specific project. Hence, the following steps are to be executed in the same order: Create a new project. Create a data set. Run the bq load command to load the data into a table. The first step is to create a project. Sign in on the BigQuery Web UI. Click on the hamburger button ( ) and select APIs Services. Click Create Project and provide a project name (Let’s say ‘LIKE.TG _project’). Now you need to enable BigQuery for which search for the same and click on Enable. Your project is now created with BigQuery enabled. The next step is to create a data set. This can be quickly done using the bq command-line tool and the command is called mk. Create a new data set using the following command: $bq mk LIKE.TG _dataset At this point, you are ready to import the GCS file into a table in this data set. The load command of bq lets you do the same. It’s slightly more complicated than the mk command so let’s go through the basic syntax first. Bq load command syntax - $bq load project:dataset.table --autodetect --source_format autodetect is a parameter used to automatically detect the schema from the source file and is generally recommended. Hence, the following command should do the job for you: $bq load LIKE.TG _project:LIKE.TG _dataset.LIKE.TG _table --autodetect --source_format=CSV gs://LIKE.TG _gc/LIKE.TG .csv The GCS file gets loaded into the table LIKE.TG _table. If no table exists under the name ‘LIKE.TG _table’ the above load command creates a new table. If LIKE.TG _table is an existing table there are two types of load available to bring the source data into this table – Overwrite or Table Append. Here’s the command to overwrite or replace: $bq load LIKE.TG _project:LIKE.TG _dataset.LIKE.TG _table --autodetect --replace --source_format=CSV gs://LIKE.TG _gc/LIKE.TG .csv Here’s the command to append data: $bq load LIKE.TG _project:LIKE.TG _dataset.LIKE.TG _table --autodetect --noreplace --source_format=CSV gs://LIKE.TG _gc/LIKE.TG .csv You should be careful with the append in terms of unique key constraints as BigQuery doesn’t enforce it on its tables. Incremental load – Type 1/ Upsert In this type of incremental load, a new record from the source is either inserted as a new record in the target table or replaces an existing record in the target table. Let’s say the source (LIKE.TG .csv) looks like this: And the target table (LIKE.TG _table) looks like this: Post incremental load, LIKE.TG _table will look like this: The way to do this would be to load the LIKE.TG .csv into a separate table (staging table) first, let’s call it, LIKE.TG _intermediate. This staging table is then compared with the target table to perform the upsert as follows: INSERT LIKE.TG _dataset.LIKE.TG _table (id, name, salary, date) SELECT id, name, salary, date FROM LIKE.TG _dataset.LIKE.TG _intermediate WHERE NOT id IN (SELECT id FROM LIKE.TG _dataset.LIKE.TG _intermediate); UPDATE LIKE.TG _dataset.LIKE.TG _table h SET h.name = i.name, h.salary = i.salary, h.date = i.date FROM LIKE.TG _dataset.LIKE.TG _intermediate i WHERE h.id = i.id; Incremental load – Type 2/ Append Only In this type of incremental load, a new record from the source is always inserted into the target table if at least one of the fields has a different value from the target. This is quite useful to understand the history of data changes for a particular field and helps drive business decisions. Let’s take the same example as before. The target table in this scenario would look like the following: To write the code for this scenario, you first insert all the records from the source to the target table as below: INSERT LIKE.TG _dataset.LIKE.TG _table (id, name, salary, date) SELECT id, name, salary, date FROM LIKE.TG _dataset.LIKE.TG _intermediate; Next, you delete theduplicate records (all fields have the same value) using the window function like this: DELETE FROM (SELECT id, name, salary, date, ROW_NUMBER() OVER(PARTITION BY id, name, salary, date) rn FROM LIKE.TG _dataset.LIKE.TG _table) WHERE rn <> 1; Hurray! You have successfully migrated your data from DynamoDB to BigQuery. Limitations of Moving Data from DynamoDB to BigQuery using Custom Code Method As you have seen now, Data Replication from DynamoDB to BigQuery is a lengthy and time-consuming process. Furthermore, you have to take care of the following situations: The example discussed in this article is to demonstrate copying over a single file from DynamoDB to BigQuery. In reality, hundreds of tables would have to be synced periodically or close to real-time; to manage that and not be vulnerable to data loss and data inconsistencies is quite the task. There are sometimes subtle, characteristic variations between services, especially when the vendors are different. It could happen in file Size Limits, Encoding, Date Format, etc. These things may go unnoticed while setting up the process and if not taken care of before kicking off Data Migration, it could lead to loss of data. So, to overcome these limitations to migrate your data from DynamoDB to BigQuery, let’s discuss an easier alternative – LIKE.TG . An easier approach to move data from DynamoDB to BigQuery using LIKE.TG The tedious task of setting this up as well as the points of concern mentioned above does not make the ‘custom method’ endeavor a suggestible one. You can save a lot of time and effort by implementing an integration service like LIKE.TG and focus more on looking at the data and generating insights from it.Here is how you can migrate your data from DynamoDB to BigQuery using LIKE.TG : Connect and configure your DynamoDB Data Source. Select the Replication mode: (i) Full dump (ii) Incremental load for append-only data (iii) Incremental load for mutable data. Configure your Google BigQuery Data Warehouse where you want to move data. SIGN UP HERE FOR A 14-DAY FREE TRIAL! Conclusion In this article, you got a detailed understanding of how to export DynamoDB to BigQuery using Custom code. You also learned some of the limitations associated with this method. Hence, you were introduced to an easier alternative- LIKE.TG to migrate your data from DynamoDB to BigQuery seamlessly. With LIKE.TG , you can move data in real-time from DynamoDb to BigQuery in a reliable, secure, and hassle-free fashion. In addition to this, LIKE.TG has 150+ native data source integrations that work out of the box. You could explore the integrations here. VISIT OUR WEBSITE TO EXPLORE LIKE.TG Before you go ahead and take a call on the right approach to move data from DynamoDB to BigQuery, you should try LIKE.TG for once. SIGN UP to experience LIKE.TG ’s hassle-free Data Pipeline platform. Share your experience of moving data from DynamoDB to BigQuery in the comments section below!

With the advent of modern-day cloud infrastructure, many business-critical applications like databases, ERPs, and Marketing applications have all moved to the cloud. With this, most of the business-critical data now resides in the cloud. Now that all the business data resides on the cloud, companies need a data warehouse that can seamlessly store the data from all the different cloud-based applications. This is where Cloud Data Warehouse comes into the picture.This post aims to help you understand what a cloud data warehouse is, its evolution, and its need. Here are the key things that this post covers: What is a Cloud Data Warehouse? A data warehouse is a repository of the current and historical information that has been collected. The data warehouse is an information system that forms the core of an organization’s business intelligence infrastructure. It is a Relational Database Management System (RDBMS) that allows for SQL-like queries to be run on the information it contains. Unlike a database, a data warehouse is optimized to run analytical queries on large data sets. A database is more often used as a transaction processing system. You can read more about the need for a data warehouse here. A Cloud Data Warehouse is a database that is delivered as a managed service in the public cloud and is optimized for analytics, scale, and usability. Cloud-based data warehouses allow businesses to focus on running their businesses rather than managing a server room, and they enable business intelligence teams to deliver faster and better insights due to improved access, scalability, and performance. Key features of Cloud Data Warehouse Some of the key features of a Data Warehouse in the Cloud are as follows: Massive Parallel Processing (MPP): MPP architectures are used in cloud-based data warehouses that support big data projects to provide high-performance queries on large data volumes. MPP architectures are made up of multiple servers that run in parallel to distribute processing and input/output (I/O) loads. Columnar data stores: MPP data warehouses are typically columnar stores, which are the most adaptable and cost-effective for analytics. Columnar databases store and process data in columns rather than rows, allowing aggregate queries, which are commonly used for reporting, to run much faster. Simplify Data Analysis with LIKE.TG ’s No-code Data Pipeline 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. LIKE.TG supports 150+ data sources 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. Get Started with LIKE.TG for free Check out why LIKE.TG is the Best: 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. Sign up here for a 14-day Free Trial! What are the capabilities of the Cloud Data Warehouse? For all the Cloud based Data Warehouse services, the cloud vendor or data warehouse provider provides the following “out-of-the-box” capabilities. Data storage and management: data is stored in a file system hosted in the cloud (i.e. S3). Automatic Upgrades: There is no such thing as a “version” or a software upgrade. Capacity management: Youcan easily expand (or contract) your data footprint. Traditional Data Warehouse vs. Cloud Data Warehouse Traditional Data Warehouse is also an on-premise Data Warehouse that is located or installed at the company’s office. Companies need to purchase hardware such as servers by themselves. The installation requires human resources and much time. The organization requires a separate staff to manage and update the Traditional Data Warehouse. Scaling the Warehouse takes time as new hardware needs to be shipped to the destination and then installation. Cloud Data Warehouse, as the name suggests is the Data Warehouse solution available on the cloud. Companies don’t have to own hardware and maintain it. All the updates, maintenance, and scalability of hardware are managed by 3rd party Cloud Data Warehouse Service providers such as Google BigQuery, Snowflake, etc. Because of the availability of data on the cloud, companies can easily integrate Cloud Data Warehouses with other SaaS (Software as a Service) platforms and tools for Business Analytics. What are the Benefits of a Cloud Data Warehouse? Previously, if an organization needed data warehousing capabilities then that would require, firstly, either building and configuring an on-site server or renting servers off-site and, secondly, configuring the connections between relevant assets. Either option requires a significant capital outlay. Cloud-based data warehouses minimize these issues.Cloud-based Data Warehousing services are offered at varying price points that are a fraction of what the previous options would cost in terms of capital, time, and stress. Apart from ease of implementation, cloud-based data warehouse solutions also offered scalability. Previous iterations would require building capacity that took possible future growth into consideration. With cloud-based data warehouses, that question is now redundant as your package can be easily scaled to your needs, no matter how they fluctuate over time (as long as it’s within the service’s limits). What are the Top 5 Cloud Data Warehouse Services? There are many cloud data warehouse solutions. According to IT Central Station, the top 5 cloud data warehouse providers are: Google BigQuery Snowflake Amazon Redshift Microsoft Azure SQL Data Warehouse Oracle Autonomous Data Warehouse What are the Challenges of a Cloud Data Warehouse? Security is a concern for cloud-based data warehousing. This is specifically due to the fact that service providers have access to their customer’s data. While service agreements and public legislation around data privacy do exist, it must be borne in mind that it is possible that these entities could, accidentally or deliberately, alter or delete the data. Another major security concern is the penetration of cloud systems by hackers who are constantly searching for and exploiting vulnerabilities in these systems in order to gain access to users’ personal data and data belonging to large corporations. Providers take maximum precautions in protecting users’ data. To this end, users are also offered choices in how their data is stored, such as having it encrypted in order to prevent unauthorized access. Given the large variety of applications, businesses use today, loading all this data present in different formats into a data warehouse is a huge task for engineers. However, fully-managed data integration platforms like LIKE.TG Data (Features and 14-day free trial) help easily mitigate this problem by providing an easy, point-and-click platform to load data to the warehouse. How to Choose the Right Cloud Data Warehouse Making the right choice necessitates a deeper understanding of how these data warehouses operate based on features such as: Architecture: elasticity, support for technology, isolation, and security Scalability: scale efficiency, elastic scale, query, and user concurrency. Performance: Query, indexing, data type, and storage optimization Use Cases: Reporting, dashboards, ad hoc, operations, and customer-facing analytics Cost: Administration, vendor pricing, infrastructure resources You should also evaluate each cloud data warehouse in terms of the use cases it must support. Here are a few examples: Reporting by analysts against historical data. Analyst-created dashboards based on historical or real-time data. Ad hoc Analytics within dashboards or other tools for interactive analysis on the fly. High-performance analytics for very large or complex queries involving massive data sets. Using semi-structured or unstructured data for Big Data Analytics. Data processing is performed as part of a data pipeline in order to deliver data downstream. Leveraging the concept of Machine Learning to train models against data in data lakes or warehouses. Much larger groups of employees require operational analytics to help them make better, faster decisions on their own. Customer-facing analytics are delivered to customers as (paid) service-service analytics. Cloud Data Warehouse Automation – What you Need to Know To accelerate the availability of analytics-ready data, some modern data integration platforms automate the entire data warehouse lifecycle. A model-driven approach will also assist your data engineers in designing, deploying, managing, and cataloging purpose-built cloud data warehouses more quickly than traditional solutions. The 3 key productivity drivers of an agile data warehouse are as follows: Ingestion and updating of data in real-time: A simple and universal solution for continuously and in real-time ingesting your enterprise data into popular cloud-based data warehouses. Workflow automation: A model-driven approach to constantly improving data warehouse operations. Trusted, enterprise-ready data: To securely share your data marts, use a smart, enterprise-scale data catalog. FAQ about Cloud Data Warehouse 1) What is the Data Warehouse lifecycle? The Data Warehouse lifecycle encompasses all phases of developing and operating a data warehouse, including: Discovery: Understanding business requirements and the data sources required to meet those requirements. Design: Designing and testing the data warehouse model iteratively Development: Writing or generating the schema and code required to build and load the data warehouse. Deployment: Putting the data warehouse into production so that business analysts can access the information. Operation: Monitoring and managing the data warehouse’s operations and performance. Enhancement: Changes are made to support changing business and technology needs. 2) What is Data Warehouse automation? Historically, data warehouses were designed, developed, deployed, operated, and revised manually by teams of developers. The average data warehouse project, from requirements gathering to product availability, could take years to complete, with a high risk of failure. Data warehouse automation makes use of metadata, data warehousing methodologies, pattern detection, and other technologies to provide developers with templates and wizards that auto-generate designs and coding that was previously done by hand. Automation automates the data warehouse lifecycle’s repetitive, time-consuming, and manual design, development, deployment, and operational tasks. IT teams can deliver and manage more data warehouse projects than ever before, much faster, with less project risk, and at a lower cost by automating up to 80% of the lifecycle. Conclusion This article provided a comprehensive guide on a Cloud Data Warehouse. It also explained the benefits and needs of a Cloud Data Warehouse in detail. It also lists the top Cloud Data Warehouse Services in the market today. With the complexity involves in Manual Integration, businesses are leaning more towards Automated and Continuous 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 your Data Analysis seamlessly. 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 firsthand. Share your experience of understanding Cloud Data Warehouses in the comments section below!

With the advent of modern-day cloud infrastructure, many business-critical applications like databases, ERPs, and Marketing applications have all moved to the cloud. With this, most of the business-critical data now resides in the cloud. Now that all the business data resides on the cloud, companies need a data warehouse that can seamlessly store the data from all the different cloud-based applications. This is where Cloud Data Warehouse comes into the picture.This post aims to help you understand what a cloud data warehouse is, its evolution, and its need. Here are the key things that this post covers: What is a Cloud Data Warehouse? A data warehouse is a repository of the current and historical information that has been collected. The data warehouse is an information system that forms the core of an organization’s business intelligence infrastructure. It is a Relational Database Management System (RDBMS) that allows for SQL-like queries to be run on the information it contains. Unlike a database, a data warehouse is optimized to run analytical queries on large data sets. A database is more often used as a transaction processing system. You can read more about the need for a data warehouse here. A Cloud Data Warehouse is a database that is delivered as a managed service in the public cloud and is optimized for analytics, scale, and usability. Cloud-based data warehouses allow businesses to focus on running their businesses rather than managing a server room, and they enable business intelligence teams to deliver faster and better insights due to improved access, scalability, and performance. Key features of Cloud Data Warehouse Some of the key features of a Data Warehouse in the Cloud are as follows: Massive Parallel Processing (MPP): MPP architectures are used in cloud-based data warehouses that support big data projects to provide high-performance queries on large data volumes. MPP architectures are made up of multiple servers that run in parallel to distribute processing and input/output (I/O) loads. Columnar data stores: MPP data warehouses are typically columnar stores, which are the most adaptable and cost-effective for analytics. Columnar databases store and process data in columns rather than rows, allowing aggregate queries, which are commonly used for reporting, to run much faster. Simplify Data Analysis with LIKE.TG ’s No-code Data Pipeline 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. LIKE.TG supports 150+ data sources 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. Get Started with LIKE.TG for free Check out why LIKE.TG is the Best: 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. Sign up here for a 14-day Free Trial! What are the capabilities of the Cloud Data Warehouse? For all the Cloud based Data Warehouse services, the cloud vendor or data warehouse provider provides the following “out-of-the-box” capabilities. Data storage and management: data is stored in a file system hosted in the cloud (i.e. S3). Automatic Upgrades: There is no such thing as a “version” or a software upgrade. Capacity management: Youcan easily expand (or contract) your data footprint. Traditional Data Warehouse vs. Cloud Data Warehouse Traditional Data Warehouse is also an on-premise Data Warehouse that is located or installed at the company’s office. Companies need to purchase hardware such as servers by themselves. The installation requires human resources and much time. The organization requires a separate staff to manage and update the Traditional Data Warehouse. Scaling the Warehouse takes time as new hardware needs to be shipped to the destination and then installation. Cloud Data Warehouse, as the name suggests is the Data Warehouse solution available on the cloud. Companies don’t have to own hardware and maintain it. All the updates, maintenance, and scalability of hardware are managed by 3rd party Cloud Data Warehouse Service providers such as Google BigQuery, Snowflake, etc. Because of the availability of data on the cloud, companies can easily integrate Cloud Data Warehouses with other SaaS (Software as a Service) platforms and tools for Business Analytics. What are the Benefits of a Cloud Data Warehouse? Previously, if an organization needed data warehousing capabilities then that would require, firstly, either building and configuring an on-site server or renting servers off-site and, secondly, configuring the connections between relevant assets. Either option requires a significant capital outlay. Cloud-based data warehouses minimize these issues.Cloud-based Data Warehousing services are offered at varying price points that are a fraction of what the previous options would cost in terms of capital, time, and stress. Apart from ease of implementation, cloud-based data warehouse solutions also offered scalability. Previous iterations would require building capacity that took possible future growth into consideration. With cloud-based data warehouses, that question is now redundant as your package can be easily scaled to your needs, no matter how they fluctuate over time (as long as it’s within the service’s limits). What are the Top 5 Cloud Data Warehouse Services? There are many cloud data warehouse solutions. According to IT Central Station, the top 5 cloud data warehouse providers are: Google BigQuery Snowflake Amazon Redshift Microsoft Azure SQL Data Warehouse Oracle Autonomous Data Warehouse What are the Challenges of a Cloud Data Warehouse? Security is a concern for cloud-based data warehousing. This is specifically due to the fact that service providers have access to their customer’s data. While service agreements and public legislation around data privacy do exist, it must be borne in mind that it is possible that these entities could, accidentally or deliberately, alter or delete the data. Another major security concern is the penetration of cloud systems by hackers who are constantly searching for and exploiting vulnerabilities in these systems in order to gain access to users’ personal data and data belonging to large corporations. Providers take maximum precautions in protecting users’ data. To this end, users are also offered choices in how their data is stored, such as having it encrypted in order to prevent unauthorized access. Given the large variety of applications, businesses use today, loading all this data present in different formats into a data warehouse is a huge task for engineers. However, fully-managed data integration platforms like LIKE.TG Data (Features and 14-day free trial) help easily mitigate this problem by providing an easy, point-and-click platform to load data to the warehouse. How to Choose the Right Cloud Data Warehouse Making the right choice necessitates a deeper understanding of how these data warehouses operate based on features such as: Architecture: elasticity, support for technology, isolation, and security Scalability: scale efficiency, elastic scale, query, and user concurrency. Performance: Query, indexing, data type, and storage optimization Use Cases: Reporting, dashboards, ad hoc, operations, and customer-facing analytics Cost: Administration, vendor pricing, infrastructure resources You should also evaluate each cloud data warehouse in terms of the use cases it must support. Here are a few examples: Reporting by analysts against historical data. Analyst-created dashboards based on historical or real-time data. Ad hoc Analytics within dashboards or other tools for interactive analysis on the fly. High-performance analytics for very large or complex queries involving massive data sets. Using semi-structured or unstructured data for Big Data Analytics. Data processing is performed as part of a data pipeline in order to deliver data downstream. Leveraging the concept of Machine Learning to train models against data in data lakes or warehouses. Much larger groups of employees require operational analytics to help them make better, faster decisions on their own. Customer-facing analytics are delivered to customers as (paid) service-service analytics. Cloud Data Warehouse Automation – What you Need to Know To accelerate the availability of analytics-ready data, some modern data integration platforms automate the entire data warehouse lifecycle. A model-driven approach will also assist your data engineers in designing, deploying, managing, and cataloging purpose-built cloud data warehouses more quickly than traditional solutions. The 3 key productivity drivers of an agile data warehouse are as follows: Ingestion and updating of data in real-time: A simple and universal solution for continuously and in real-time ingesting your enterprise data into popular cloud-based data warehouses. Workflow automation: A model-driven approach to constantly improving data warehouse operations. Trusted, enterprise-ready data: To securely share your data marts, use a smart, enterprise-scale data catalog. FAQ about Cloud Data Warehouse 1) What is the Data Warehouse lifecycle? The Data Warehouse lifecycle encompasses all phases of developing and operating a data warehouse, including: Discovery: Understanding business requirements and the data sources required to meet those requirements. Design: Designing and testing the data warehouse model iteratively Development: Writing or generating the schema and code required to build and load the data warehouse. Deployment: Putting the data warehouse into production so that business analysts can access the information. Operation: Monitoring and managing the data warehouse’s operations and performance. Enhancement: Changes are made to support changing business and technology needs. 2) What is Data Warehouse automation? Historically, data warehouses were designed, developed, deployed, operated, and revised manually by teams of developers. The average data warehouse project, from requirements gathering to product availability, could take years to complete, with a high risk of failure. Data warehouse automation makes use of metadata, data warehousing methodologies, pattern detection, and other technologies to provide developers with templates and wizards that auto-generate designs and coding that was previously done by hand. Automation automates the data warehouse lifecycle’s repetitive, time-consuming, and manual design, development, deployment, and operational tasks. IT teams can deliver and manage more data warehouse projects than ever before, much faster, with less project risk, and at a lower cost by automating up to 80% of the lifecycle. Conclusion This article provided a comprehensive guide on a Cloud Data Warehouse. It also explained the benefits and needs of a Cloud Data Warehouse in detail. It also lists the top Cloud Data Warehouse Services in the market today. With the complexity involves in Manual Integration, businesses are leaning more towards Automated and Continuous 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 your Data Analysis seamlessly. 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 firsthand. Share your experience of understanding Cloud Data Warehouses in the comments section below!

Do you rely heavily on GA4 data for analyzing the metrics of your website engagement? If yes, then you would face problems while collecting all the GA4 data and performing advanced analytics on it. If you want to gain business-critical insights from your GA4 data, then you can’t simply manipulate it. You need to have access to all your marketing and website data in a centralized repository This article throws light on two methods for implementing GA4 BigQuery Integration. However, to increase your time to value you can definitely go through the simple two-step process for replicating data from GA4 to BigQuery. What is BigQuery? Google Cloud Platform provides BigQuery, Google’s enterprise data warehouse that makes large-scale data analysis accessible to everyone. It is a platform-as-a-service (PaaS) that supports querying using ANSI SQL. It’s a fully managed and serverless data warehouse that empowers you to focus on analytics instead of managing infrastructure. Advantages of Connecting Google Analytics 4 to BigQuery 1. Raw, Unsampled Data for Better Analysis Exporting data to BigQuery allows you to access raw, unsampled data and perform more precise and detailed analysis than the aggregated data available directly in GA4. 2. Extended Retention Period With bigQuery, you can store your GA4 data for as long as you need, beyond the default retention periods in GA4. This extended retention allows you to conduct historical analysis and identify long-term trends, providing security and ease with your data storage. 3. Joins with Other Data Sources In BigQuery, you can join GA4 data with data from other sources, such as your CRM, sales databases, or third-party APIs. This capability facilitates comprehensive, cross-platform analysis, giving you a more insightful and knowledgeable view of your business performance. 4. Advanced Visualization BigQuery integrates seamlessly with visualization tools like Google Data Studio, Tableau, and Looker. These tools allow you to create sophisticated dashboards and reports, enabling more profound insights and informed decision-making. 5. GA4 BigQuery Export is free Google offers free exports of GA4 data to BigQuery, making it an economical choice for businesses to leverage advanced analytics capabilities without incurring additional costs. Integrate Google Analytics to BigQueryGet a DemoTry itIntegrate Google Ads to RedshiftGet a DemoTry itIntegrate Salesforce to PostgreSQLGet a DemoTry it Methods to connect Google Analytics 4 to BigQuery? Using LIKE.TG Data to Set up GA4 BigQuery Integration LIKE.TG Datahelps you directly transfer data from GA4 and150+ other sourcesto a Data Warehouse such as Google BigQuery, 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. Using Google Cloud Platform API to Implement GA4 BigQuery Integration The APIs will allow you to do the integration of data by configuring a connection between GA4 and your other data system. It allows data streaming in real-time. But, as the process is highly complex and time consuming, it consumes a lot of bandwidth. Using CSV files This method uses the native capability of GA4 to export file into CSV and then move to BigQuery. For one time migration, and small data volume which doesn’t require any modification, this is a highly recommended method. Watch our latest on-demand webinar for a hands-on to the Google Analytics 4 + BigQuery export. LIKE.TG plateform can help how to use querie from basic to complex, explore new event tables and updated schema, and Explore ways to discover and extract the metrics necessary for driving your business forward. How to Set up GA4 BigQuery Integration Using Three Methods? Method 1: Using LIKE.TG Data to Set up GA4 BigQuery Integration Method 2: Using Google Cloud Platform to Implement GA4 BigQuery Integration Method 3: Using CSV files Method 1: Using LIKE.TG Data to Set up GA4 BigQuery Integration LIKE.TG takes care of all your data preprocessing to set up GA4 BigQuery Integration and lets you focus on key business activities and draw a much more powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent reliable solution to manage data in real-time and always has analysis-ready data in your desired destination. LIKE.TG Data focuses on two simple steps to connect GA4 BigQuery Integration: Step 1: Configure Google Analytics 4 as a Source Step 2: Integrate Data into Google BigQuery Step 1: Configure GA4 as a Source ClickPIPELINESin theNavigation Bar. Click+ CREATEin thePipelines List View. In theSelect Source Typepage, selectGoogle Analytics 4as the Source. In theConfigure your Google Analytics 4 Accountpage, do one of the following: Select a previously configured account and clickCONTINUE. Click+ ADD GOOGLE ANALYTICS 4 ACCOUNTand perform the following steps to configure an account: Select your linked Google account. ClickAllowto grant LIKE.TG access to your analytics data. In theConfigure your Google Analytics 4 Sourcepage, specify the following: Step 2: Integrate Data into Google BigQuery ClickDESTINATIONSin theNavigation Bar. Click+ CREATEin theDestinations List View. InAdd Destinationpage selectGoogleBigQueryas the Destination type. In theConfigure your GoogleBigQuery Warehousepage, specify the following details: As can be seen, you are simply required to enter the corresponding credentials to implement this fully automated data pipeline without using any code. Method 2: Using Google Cloud Platform to Implement GA4 BigQuery Integration The steps to set up GA4 Bigquery Integration are as follows: Step 1: Create a Project in Google BigQuery Step 2: Enable GA4 BigQuery Linking Step 3: Enable Google Cloud API Step 4: Add a Service account Step 5: Use Google BigQuery with GA4 data Step 1: Create a Project in Google BigQuery Log in to your Google BigQuery account. On the menu bar, click on the arrow beside the name of the project getting displayed. A pop-up window will appear with a list of existing BigQuery projects. In the top-left section of the pop-up window, click on the “New Project” option. The New Project window appears. Now, you can set the name and the location of the project. Then click on the “Create” button and the project will be created. Step 2: Enable GA4 BigQuery Linking Log in to your Google Analytics account. For further information about Google Analytics. Click on Google Analytics 4 Admin option, found in the bottom-left corner of the window. Now, after going to the GA4 Admin panel, click on “BigQuery Linking”. BigQuery Linking window appears. Now, click on the “Link” button beside the search bar. “Create a link with BigQuery” window appears. Now, click on the learn more link. In the next window, scroll down and copy the Service Account Id of the service account given in point 5 of step 1 ([email protected]). Now, go back to the “Create a link with BigQuery” window. Then click on the “Choose a BigQuery project” option. Then select the name of the BigQuery project that you want to link with Google Analytics 4. Now, select the Data Location from the drop-down menu. Click on the “Next” button. Now, select the type of Data streams. If you have a mobile app and want to export the user ids to Google BigQuery, you may additionally choose “Include advertising Identifiers for mobile app streams.” Select the frequency of data movement accordingly i.e, either Daily (once a day) or Streaming (continuous export). Now, click on the “Next” button and then review your choices and click on the “Submit” button. Now, the link for the GA4 BigQuery is created. Until now, just the GA4 BigQuery linking is accomplished. But they are still not connected. So, you need to create an API. Step 3: Enable Google Cloud API Go to the Google Cloud Console. Then in the left navigation pane, go to API Services and select “Library”. The API Library page appears. Now, if you have not selected the project, then click on the current project name at the top. A separate window with the list of projects appears. Select the project you want to link. Now, in the search bar, search for BigQuery API and click on it. Now, make sure the BigQuery API is enabled and click on the “Manage” button. Step 4: Add a Service Account From the sidebar menu, select “Credentials”. Go to “Create Credentials” and then select the “Service account” option. In the Service account name, type [email protected], i.e., the Id already copied in step 2. Then in the Service account ID, write the ID where you want to give access to that account and click on “Create”. Now, grant the editor access to the Service account. And then click on “Continue” and select “Done”. Step 5: Use Google BigQuery with GA4 Data After all the procedures, wait for 24 hours for the data set to export to your BigQuery project. You’ll find 2 tables with each dataset. One for continuous export of raw events throughout the day and another for full daily export of events. Now, you can run SQL queries on the tables according to your requirements. Reasons for linking failures Linking to BigQuery can fail for either of the following two reasons: Your organization’s policy prohibits export to the United States. Choose a different location if you’ve chosen the United States as the location of your data. Modify your organization’s policy if your organization policy prohibits service accounts from the domain you want to export data from. Reasons for export failures There are several reasons due to which your GA4 BigQuery Schema Export may fail, such as: Method 3: Using CSV Files This method for integration is useful for one time migrations. Suppose, you have your 100 customer’s data in a google sheet. You don’t have to build data pipelines to move that to BigQuery. CSV is the best option there. The steps to connect are: Log in to your report in Google Analytics, and load the report you want to move to BigQuery. Click on the Share button at the top-right corner of the screen. Select download file and choose CSV as the file format. After downloading the file, you can import or Loading CSV data from Cloud Storage the file into BigQuery using one of the available methods. Types of GA4 BigQuery exports Streaming Export This export refers to ingesting real-time data to BigQuery for analysis within a few minutes. This is a viable option for businesses that rely on real-time data analysis for their business decisions or require up-to-the-minute data, such as real-time reporting, monitoring user behavior as it happens, and quickly identifying and responding to emerging trends. Daily Export This export refers to exporting a complete data set and transferring it to BigQuery in 24-hour cycles. This method is cost-effective and sufficient for most reporting and analysis needs that do not require real-time data. It allows for comprehensive daily analysis without the higher costs associated with continuous data transfer. GA4 BigQuery Export Schema BigQuery provides a schema format for all the data exported from GA4 to BigQuery. When the data is exported from GA4 to BigQuery, a dataset prefixed by ‘analytics_’ is automatically created in BigQuery. Each day, a new table containing the previous day’s data is created within this dataset. These tables are named events_YYYYMMDD, followed by the date they represent. For example, the table for October 15th 2023, would be named events_20231015. Those who have activated the Streaming export feature will notice an additional table labeled events_intraday _YYYYMMDD. This table is updated in near real-time and replaced daily with a new one. In the BigQuery interface, these individual tables are displayed collectively under a single name, simplifying the visual representation. Which of these methods allows you to load GA4 historical data to BigQuery? You can use LIKE.TG ’s automated data pipeline platform or Google Analytics API to connect to BigQuery. You can get into the user level detail with GA API. But, it requires more steps to extract and load the data. As LIKE.TG ’s pipelines are automated, the effort and time will be much lesser. It also gives you the flexibility to decide the period of historic load based on your use case. And, moving the historical data is free of cost. Which Google Analytics properties data can you export to BigQuery? A property in Google Analytics implies a website, blog, or application having a distinct tracking ID. In your GA account. You can decide the number of properties based on your use case.. Using the above methods, you can export the details of these properties to your BigQuery. While configuring your source using LIKE.TG Data, you will have the option to select your property. After you’ve exported Google Analytics data to BigQuery, what can you achieve with the data? By migrating your data from GA4 to BigQuery, you will be able to help your business stakeholders find the answers to these questions: Which Demographic contributes to the highest fraction of users of a particular Product Feature? How are Paid Sessions and Goal Conversion Rate varying with Marketing Spend and Cash in-flow? How to identify your most valuable customer segments? Why should you enable the BigQuery linking for GA4? There are several reasons to allow BigQuery linking for GA4, such as: To store your data in BigQuery (Google Cloud) and/or send it to your data warehouse in other clouds like Azure or Snowflake To join and enrich your data with other marketing or contextual data To visualize your data in tools like Tableau or PowerBI To perform advanced analysis To use your data as input for (machine learning) models Additional Resources on GA4 to Bigquery Explore more on Bigquery to Bigquery Migration Export Google Analytics Data Key Takeaways This article has discussed 3 methods for setting up GA4 BigQuery Integration. If you can take all the responsibility for implementing this integration, you can continue with the manual method. However, if you want a more seamless integration that is fully automated and completely managed, you should definitely give LIKE.TG a try. FAQ on Integrate Data from GA4 to BigQuery Is BigQuery free with GA4? Everyone who owns a GA4 property i.e. Premium or Standard has access to BigQuery. So, unlike earlier versions of Google Analytics, with GA4, users don’t need to pay an extra fee to connect their GA4 property to their BigQuery project. How to query GA4 data in BigQuery? 1. After setting up GA4 BigQuery integration, you can easily query your raw events data in BigQuery. You need to go to Google Data Studio and select BigQuery.2. You can see the list of all the Google Cloud Projects to which you have access. From there you can navigate to the tables and columns.3. For queries, click on SQL Workspace, and type your queries to filter and display the GA4 data according to your requirements. What is the export limit for GA4 BigQuery? 1. GA4 BigQuery supports a free tier and a paid tier plan for its users, and the export limit for each of them varies based on the type of export the user performs. For example, if the user has selected a free tier plan, his export limit for daily export data would be 1 million daily events.2. If the user belongs to the paid tier plan, then he will not have any export limit on his data. However, charges would be applied based on storage and query usage. The streaming export feature is available only in the paid tier plan. How to backfill data from GA4 to BigQuery? 1. Set up GA4 and BigQuery Integration2. Export Historical data3. Use GA4 API and export the data to a CSV or JSON file.4. Import data to BigQuery.

var source_destination_email_banner = 'true'; In a time where data is being termed the new oil, businesses need to have a data management system that suits their needs perfectly and positions them to be able to take full advantage of the benefits of being data-driven. Data is being generated at rapid rates and businesses need database systems that can scale up and scale down effortlessly without any extra computational cost. Enterprises are exhausting a huge chunk of their data budgets in just maintaining their present physical database systems instead of directing the said budget towards gaining tangible insights from their data. This scenario is far from ideal and is the reason why moving your Oracle data to a cloud-based Data Warehouse like Google BigQuery is no longer a want but a need. This post provides a step-by-step walkthrough on how to migrate data from Oracle to BigQuery. Introduction to Oracle Oracle database is a relational database system that helps businesses store and retrieve data. Oracle DB(as it’s fondly called) provides a perfect combination of high-level technology and integrated business solutions which is a non-negotiable requisite for businesses that store and access huge amounts of data. This makes it one of the world’s trusted database management systems. Introduction to Google BigQuery Google BigQuery is a cloud-based serverless Data Warehouse for processing a large amount of data at a rapid rate. It is called serverless as it automatically scales when running, depending on the data volume and query complexity. Hence, there is no need to spend a huge part of your database budget on in-site infrastructure and database administrators. BigQuery is a standout performer when it comes to analysis and data warehousing. It provides its customers with the freedom and flexibility to create a plan of action that epitomizes their entire business structure. Performing ETL from Oracle to BigQuery There are majorly two ways of migrating data from Oracle to BigQuery. The two ways are: Method 1: Using Custom ETL Scripts to Connect Oracle to BigQuery This method involves a 5-step process of utilizing Custom ETL Scripts to establish a connection from Oracle to BigQuery in a seamless fashion. There are considerable upsides to this method and a few limitations as well. Method 2: Using LIKE.TG to Connect Oracle to BigQuery LIKE.TG streamlines the process of connecting Oracle to BigQuery, enabling seamless data transfer and transformation between the two platforms. This ensures efficient data migration, accurate analytics, and comprehensive insights by leveraging BigQuery’s advanced analytics capabilities. Get Started with LIKE.TG for Free In this post, we will cover the second method (Custom Code) in detail. Toward the end of the post, you can also find a quick comparison of both data migration methods so that you can evaluate your requirements and choose wisely. Methods to Connect Oracle to BigQuery Here are the methods you can use to set up Oracle to BigQuery migration in a seamless fashion: Method 1: Using Custom ETL Scripts to Connect Oracle to BigQuery The steps involved in migrating data from Oracle DB to BigQuery are as follows: Step 1: Export Data from Oracle DB to CSV Format Step 2: Extract Data from Oracle DB Step 3: Upload to Google Cloud Storage Step 4: Upload to BigQuery from GCS Step 5: Update the Target Table in BigQuery Let’s take a step-by-step look at each of the steps mentioned above. Step 1: Export Data from Oracle DB to CSV Format BigQuery does not support the binary format produced by Oracle DB. Hence we will have to export our data to a CSV(comma-separated value) file. Oracle SQL Developer is the preferred tool to carry out this task. It is a free, integrated development environment. This tool makes it exceptionally simple to develop and manage Oracle databases both on-premise and on the cloud. It is a migration tool for moving your database to and from Oracle. Oracle SQL Developer can be downloaded for free from here. Open the Oracle SQL Developer tool, and right-click the table name in the object tree view. Click on Export. Select CSV, and the export data window will pop up. Select the format tab and select the format as CSV. Enter the preferred file name and location. Select the columns tab and verify the columns you wish to export. Select the Where tab and add any criteria you wish to use to filter the data. Click on apply. Step 2: Extract Data from Oracle DB The COPY_FILE procedure in the DBMS_FILE_TRANSFER package is used to copy a file to a local file system. The following example copies a CSV file named client.csv from the /usr/admin/source directory to the /usr/admin/destination directory as client_copy.csv on a local file system. The SQL command CREATE DIRECTORY is used to create a directory object for the object you want to create the CSV file. For instance, if you want to create a directory object called source for the /usr/admin/source directory on your computer system, execute the following code block CREATE DIRECTORY source AS '/usr/admin/source'; Use the SQL command CREATE DIRECTORY to create a directory object for the directory into which you want to copy the CSV file. An illustration is given below CREATE DIRECTORY dest_dir AS '/usr/admin/destination'; Where dest_dir is the destination directory Grant required access to the user who is going to run the COPY_FILE procedure. An illustration is given below: GRANT EXECUTE ON DBMS_FILE_TRANSFER TO admin; GRANT READ ON DIRECTORY source TO admin; GRANT WRITE ON DIRECTORY client TO admin; Connect as an admin user and provide the required password when required: CONNECT admin Execute the COPY_FILE procedure to copy the file: BEGIN DBMS_FILE_TRANSFER.COPY_FILE( source_directory_object => 'source', source_file_name => 'client.csv', destination_directory_object => 'dest_dir', destination_file_name => 'client_copy.csv'); END; Step 3: Upload to Google Cloud Storage Once the data has been extracted from Oracle DB the next step is to upload it to GCS. There are multiple ways this can be achieved. The various methods are explained below. Using Gsutil GCP has built Gsutil to assist in handling objects and buckets in GCS. It provides an easy and unique way to load a file from your local machine to GCS. To copy a file to GCS: gsutil cp client_copy.csv gs://my-bucket/path/to/folder/ To copy an entire folder to GCS: gsutil dest_dir -r dir gs://my-bucket/path/to/parent/ Using Web console An alternative means to upload the data from your local machine to GCS is using the web console. To use the web console alternative follow the steps laid out below. Login to the GCP using the link. You ought to have a working Google account to make use of GCP. Click on the hamburger menu which produces a drop-down menu. Hit on storage and navigate to the browser on the left tab. Create a new bucket to which you will migrate your data. Make sure the name you choose is globally unique. Click on the bucket you created and select Upload files. This action takes you to your local directory where you choose the file you want to upload. The data upload process starts immediately and a progress bar is shown. Wait for completion, after completion the file will be seen in the bucket. Step 4: Upload to BigQuery from GCS To upload to BigQuery you make use of either the web console UI or the command line. Let us look at a brief on both methods. First, let’s let look into uploading the data using the web console UI. The first step is to go to the BigQuery console under the hamburger menu. Create a dataset and fill out the drop-down form. Click and select the data set created by you. An icon showing ‘create table’ will appear below the query editor. Select it. Fill in the drop-down list and create the table. To finish uploading the table, the schema has to be specified. This will be done using the command-line tool. When using the command line interacting with GCS is a lot easier and straightforward. To access the command line, when on the GCS home page click on the Activate cloud shell icon shown below. The syntax of the bq command line is shown below: bq --location=[LOCATION] load --source_format=[FORMAT] [DATASET].[TABLE] [PATH_TO_SOURCE] [SCHEMA] [LOCATION] is an optional parameter that represents your Location. [FORMAT] is to be set to CSV. [DATASET] represents an existing dataset. [TABLE] is the table name into which you're loading data. [PATH_TO_SOURCE] is a fully-qualified Cloud Storage URI. [SCHEMA] is a valid schema. The schema must be a local JSON file or inline. Note: Instead of using supplying a schema definition, there is an autodetect flag that can be used. You can specify your scheme using the bq command line. An illustration is shown below using a JSON file bq --location=US load --source_format=CSV your_dataset.your_table gs://your_bucket/your_data.csv ./your_schema.json The schema can also be auto-detected. An example is shown below: bq --location=US load --autodetect --source_format=CSV your_dataset.your_table gs://mybucket/data.csv BigQuery command-line interface offers us 3 options to write to an existing table. This method will be used to copy data to the table we created above. The options are: a) Overwrite the table bq --location=US load --autodetect --replace --source_format=CSV your_dataset_name.your_table_name gs://bucket_name/path/to/file/file_name.csv b) Append the table bq --location=US load --autodetect --noreplace --source_format=CSV your_dataset_name.your_table_name gs://bucket_name/path/to/file/file_name.csv ./schema_file.json c) Add a new field to the target table. In this code, the schema will be given an extra field. bq --location=asia-northeast1 load --noreplace --schema_update_option=ALLOW_FIELD_ADDITION --source_format=CSV your_dataset.your_table gs://mybucket/your_data.csv ./your_schema.json Step 5: Update the Target Table in BigQuery The data that was joined in the steps above have not been fully updated to the target table. The data is stored in an intermediate data table, this is because GCS is a staging area for BigQuery upload. Hence, the data is stored in an intermediate table before being uploaded to BigQuery: There are two ways of updating the final table as explained below. Update the rows in the final table and insert new rows from the intermediate table. UPDATE final_table t SET t.value = s.value FROM intermediate_data_table s WHERE t.id = s.id; INSERT final_table (id, value) SELECT id, value FROM intermediate_data_table WHERE NOT id IN (SELECT id FROM final_table); Delete all the rows from the final table which are in the intermediate table. DELETE final_table f WHERE f.id IN (SELECT id from intermediate_data_table); INSERT data_setname.final_table(id, value) SELECT id, value FROM data_set_name.intermediate_data_table; 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 Limitations of Using Custom ETL Scripts to Connect Oracle to BigQuery Writing custom code would add value only if you are looking to move data once from Oracle to BigQuery. When a use case that needs data to be synced on an ongoing basis or in real-time from Oracle into BigQuery arises, you would have to move it in an incremental format. This process is called Change Data Capture. The custom code method mentioned above fails here. You would have to write additional lines of code to achieve this. When you build custom SQL scripts to extract a subset of the data set in Oracle DB, there is a chance that the script breaks as the source schema keeps changing or evolving. Often, there arises a need to transform the data (Eg: hide Personally Identifiable Information) before loading it into BigQuery. Achieving this would need you to add additional time and resources to the process. In a nutshell, ETL scripts are fragile with a high propensity to break. This makes the entire process error-prone and becomes a huge hindrance in the path of making accurate, reliable data available in BigQuery. Method 2: Using LIKE.TG to Connect Oracle to BigQuery Integrate your Data Seamlessly [email protected]"> No credit card required Using a fully managed No-Code Data Pipeline platform likeLIKE.TG can help you replicate data from Oracle to BigQuery in minutes. LIKE.TG completely automates the process of not only loading data from Oracle 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. Here are the steps to replicate data from Oracle to BigQuery using LIKE.TG : Step 1: Connect to your Oracle database by providing the Pipeline Name, Database Host, Database Port, Database User, Database Password, and Service Name. Step 2: Configure Oracle to BigQuery Warehouse migration by providing the Destination Name, Project ID, GCS Bucket, Dataset ID, Enabling Stream Inserts, and Sanitize Table/Column Names. Migrate data from Oracle to BigQueryGet a DemoTry itMigrate data from Oracle to SnowflakeGet a DemoTry itMigrate data from Amazon S3 to BigQueryGet a DemoTry it Here are more reasons to love 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. 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 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 call Live Monitoring: LIKE.TG allows you to monitor the data flow and check where your data is at a particular point in time. Conclusion This blog talks about the two methods you can use to connect Oracle to BigQuery in a seamless fashion. If you rarely need to transfer your data from Oracle to BigQuery, then the first manual Method will work fine. Whereas, if you require Real-Time Data Replication and looking for an Automated Data Pipeline Solution, then LIKE.TG is the right choice for you! Connect Oracle to Bigquery without writing any code With LIKE.TG , you can achieve simple and efficient data migration from Oracle to BigQuery in minutes. LIKE.TG can help you replicate Data from Oracle and 150+ data sources(including 50+ Free Sources) to BigQuery or a destination of your choice and visualize it in a BI tool. This makes LIKE.TG the right partner to be by your side as your business scales. 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.

Are you trying to derive deeper insights from your Amazon DynamoDB by moving the data into a larger Database like Amazon S3? Well, you have landed on the right article. Now, it has become easier to replicate data from DynamoDB to S3 using AWS Glue.Connecting DynamoDB with S3 allows you to export NoSQL data for analysis, archival, and more. In just two easy steps, you can configure an AWS Glue crawler to populate metadata about your DynamoDB tables and then create an AWS Glue job to efficiently transfer data between DynamoDB and S3 on a scheduled basis. This article will tell you how you can connect your DynamoDB to S3 using AWS Glue along with their advantages and disadvantages in the further sections. Read along to seamlessly connect DynamoDB to S3. Prerequisites You will have a much easier time understanding the steps to connect DynamoDB to S3 using AWS Glue if you have: An active AWS account. Working knowledge of Databases. A clear idea regarding the type of data to be transferred. Steps to Connect DynamoDB to S3 using AWS Glue This section details the steps to move data from DynamoDB to S3 using AWS Glue. This method would need you to deploy precious engineering resources to invest time and effort to understand both S3 and DynamoDB. They would then need to piece the infrastructure together bit by bit. This is a fairly time-consuming process. Now, let us export data from DynamoDB to S3 using AWS glue. It is done in two major steps: Step 1: Creating a Crawler Step 2: Exporting Data from DynamoDB to S3 using AWS Glue. Step 1: Create a Crawler The first step in connecting DynamoDB to S3 using AWS Glue is to create a crawler. You can follow the below-mentioned steps to create a crawler. Create a Database DynamoDB. Pick a table from the Table drop-down list. Let the table info get created through the crawler. Set up crawler details in the window below. Provide a crawler name, such asdynamodb_crawler. Add database name and DynamoDB table name. Provide the necessary IAM role to the crawler such that it can access the DynamoDB table. Here, the created IAM role is AWSGlueServiceRole-DynamoDB. You can schedule the crawler. For this illustration, it is running on-demand as the activity is one-time. Review the crawler information. Run the crawler. Check the catalog details once the crawler is executed successfully. Step 2: Exporting Data from DynamoDB to S3 using AWS Glue Since the crawler is generated, let us create a job to copy data from the DynamoDB table to S3. Here the job name given is dynamodb_s3_gluejob.In AWS Glue, you can use either Python or Scala as an ETL language. For the scope of this article, let us use Python Pick your data source. Pick your data target. Once completed, Glue will create a readymade mapping for you. Once you review your mapping, it will automatically generate python code/job for you. Execute the Python job. Once the job completes successfully, it will generate logs for you to review. Go and check the files in the bucket. Download the files. Review the contents of the file. Load Data From DynamoDB and S3 to a Data Warehouse With LIKE.TG ’s No Code Data Pipeline 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. Start for free now! Get Started with LIKE.TG for Free Advantages of Connecting DynamoDB to S3 using AWS Glue Some of the advantages of connecting DynamoDB to S3 using AWS Glue include: This approach is fully serverless and you do not have to worry about provisioning and maintaining your resources You can run your customized Python and Scala code to run the ETL You can push your event notification to Cloudwatch You can trigger the Lambda function for success or failure notification You can manage your job dependencies using AWS Glue AWS Glue is the perfect choice if you want to create a data catalog and push your data to the Redshift spectrum Disadvantages of Connecting DynamoDB to S3 using AWS Glue Some of the disadvantages of connecting DynamoDB to S3 using AWS Glue include: AWS Glue is batch-oriented and does not support streaming data. In case your DynamoDB table is populated at a higher rate. AWS Glue may not be the right option AWS Glue service is still in an early stage and not mature enough for complex logic AWS Glue still has a lot of limitations on the number of crawlers, number of jobs, etc. Refer to AWS documentation to know more about the limitations. LIKE.TG Data, on the other hand, comes with a flawless architecture and top-class features that help in moving data from multiple sources to a Data Warehouse of your choice without writing a single line of code. It offers excellent Data Ingestion and Data Replication services.Compared to AWS Glue‘s support for limited sources. LIKE.TG supports 150+ ready-to-use integrations across databases, SaaS Applications, cloud storage, SDKs, and streaming services with a flexible and transparentpricing plan. With just a five-minute setup, you can replicate data from any of your Sources to a database or data warehouse Destination of your choice. Conclusion AWS Glue can be used for data integration when you do not want to worry about your resources and do not need to take control over your resources i.e., EC2 instances, EMR cluster, etc.Thus, connecting DynamoDB to S3 using AWS Glue can help you to replicate data with ease. 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. Furthermore, you will have to build an in-house solution from scratch if you wish to transfer your data from DynamoDB or S3 to a Data Warehouse for analysis. LIKE.TG Data provides an Automated No-code Data Pipeline that empowers you to overcome the above-mentioned limitations. LIKE.TG caters to 150+ Sources BI tools (including 40+ 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. It will make your life easier and make data migration hassle-free. Learn more about 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 firsthand. Share your experience of setting up DynamoDB to S3 Integration in the comments section below!

Kipi.bi and LIKE.TG Data have been bringing increased efficiency and maturity into enterprise organizations’ data stacks for years, and both teams are thrilled to formalize their partnership! Both organizations share a keen dedication to enabling data-driven decision-making by allowing businesses to leverage the power of modern data solutions.In this data-driven era, businesses are relentlessly striving to harness the power of data for better decision-making, insights, and growth. The partnership between Kipi.bi and LIKE.TG Data aims to empower organizations to seamlessly build and manage their data stack, unlocking new possibilities in data analytics and business intelligence. Why Kipi.bi and LIKE.TG Data? As Snowflake’s 2023 America Systems Integrator Partner of the Year, Kipi.bi brings a wealth of experience in architecting and optimizing data solutions within Snowflake. Their team of seasoned professionals understands the intricacies of Snowflake’s cloud-based data platform, enabling them to tailor solutions that align with the unique needs of each client. LIKE.TG Data, on the other hand, is a leading no-code, zero-maintenance ETL platform. As a Snowflake Premier Partner, the platform is uniquely built to be able to integrate into the Snowflake data warehouse with ease. With LIKE.TG Data, businesses can set up their data ingestion into Snowflake or any other data warehouse, reduce time-to-insight, and make data accessible across the organization. The combination of Kipi.bi and LIKE.TG Data allows data-driven organizations to leverage the power of a real-time, flexible and cost-effective ELT platform along with the knowledge of a forward-thinking team of experts within the Snowflake and analytics industry. The two organizations with be working closely together to deliver a seamless experience to joint customers to leverage their data to the fullest extent. Kipi.bi has been able to utilize the powerful combination of LIKE.TG Data and Snowflake to build robust and scalable data stacks for a number of organizations, like RNDC and Clinical Ink, and they are looking forward to bringing on many more such joint customers! This is the beginning of the partner enablement series that will be conducted between kipi.bi and LIKE.TG Data, as they are committed to ensuring that their clients derive maximum value from this collaboration. Both Kipi.bi and LIKE.TG Data are excited about the possibilities that lie ahead and look forward to helping deliver transformative data solutions across the community! About kipi.bi: kipi.bi helps businesses overcome data gaps and deliver rapid insights at scale. With Snowflake at their core, they believe good data has the power to enable innovation without limits.Kipi earned Snowflake’s Americas System Integrator Growth Partner of the Year Award at the 2023 Snowflake Summit and holds 7 industry competency badges. Kipi is committed to pioneering world-class data solutions for Snowflake customers including 50+ Accelerators, Enablers, Solutions, and Native Apps to boost performance within Snowflake. Let kipi.bi become your trusted partner for data and analytics and we’ll empower your teams to access data-driven insights and unlock new revenue streams at scale. About LIKE.TG Data: LIKE.TG Data is an intuitive data pipeline platform that modern data analytics teams across 40+ countries rely on to fuel timely analytics and data-driven decisions. LIKE.TG Data helps them reliably and effortlessly sync data from 150+ SaaS apps and other data sources to any cloud warehouse or data lake and turn it analytics-ready through intuitive models and workflows.

This blog was written based on a collaborative webinar conducted by LIKE.TG Data and Danu Consulting- “Data Bytes and Insights: Building a Modern Data Stack from the Ground Up”, furthering LIKE.TG ’s partnership with Danu consulting. The webinar explored how to build a robust modern data stack that will act as a foundation towards more advanced data science applications like AI and ML. If you are interested in knowing more, visit our YouTube channel now! The Foundation for Good Data Science The general scope of data science is very broad. The hot topics in data science today are all related to ML and AI. However, this is only the tip of the iceberg- the aspirational state of data science. There is a lot that needs to go on in the background for ML and AI within an organization to be successful. What do we need to have first? We need to have a solid foundation to build up to AI capabilities within an organization. Some key questions to be answered to evaluate this foundation could be- Do we have access to the data we need? How is the required data accessed? Do we have good data governance? Do we have the infrastructure in place to implement all our required projects? Can we view and understand data easily? How can an ML/AI model go into production? 1. Digitalization, Access, and Control The first thing to understand is how data is captured within your system. This may be done through a variety of methods, from manual entry into spreadsheets to complex database systems. It’s important to consider which method will allow easiest and clearest access to data within the data stack. Next, you need to find out who has the ultimate source of truth. Formation of data silos can be a huge issue within organizations, with data from different teams displaying completely different numbers, making it very complicated to make data-driven decisions. It’s important to have a centralized source of truth that will act as the foundation for all data activities within the organization. Finally, it is important to consider how the data can be accessed. Even if the data is all captured and centralized in a common format, it is of no use unless it can be accessed easily by the necessary stakeholders. A complex and inaccessible database is of no use to the organization – data is most valuable when it is actively used to make decisions. 2. Data Governance Data governance is an iterative process between workflows, technologies and people. It is not achieved in one go but is a continuous process that needs to be continually improved. It involves a lot of change management and involves a lot of people. But with the right balance can become one of the biggest assets for the company. Understanding by all involved stakeholders on the owner of data, the processes to be followed, the technology to be used and the control measures in place can ensure that data is safe, secure and traceable. The Benefits of Having a Cloud Infrastructure There are a number of reasons why having a cloud infrastructure could prove to be beneficial for an organization’s data stack. With a good cloud analytics process, the benefits are multifold- far beyond just cost savings on the server! These include: Being process focused: A cloud infrastructure would allow an organization to focus on the processes rather than the infrastructure. Having an updated system: Being on the cloud means that an organization can always use the latest versions of tools and would not need to invest in purchasing their own infrastructure to keep up to date. Integrating data: cloud systems allow organizations to integrate their data from different sources. Enjoying Shorter time-to-market: with a cloud database, it is much easier to create endpoints for applications. Having a better user experience: generally, cloud environments have a much better UX/CX which leads to a better user experience for all involved stakeholders. Using a “sandbox” environment: cloud infrastructures often allow for the flexibility to experiment with queries, new analytics processes, products, etc. in a “sandbox” environment that can help the business hone in on what works best for them. Lowering costs: The cost of cloud infrastructure for basic functions is often quite accessible, and can be easily scaled according to the growth and requirements of the organization. Increased efficiency: Using serverless data warehouses would mean much faster queries and much more effective reporting. ELT: The Roads of the Cloud Data Infrastructure Organizations often have a multitude of data sources like on-premise and cloud databases, social media platforms, digital platforms, excel files, and others. On the other hand, the data stack on the cloud would include a cloud data lake or data warehouse, from which dashboards, reports, and ML models can be created. How can these two separate aspects be integrated to bridge the disconnect and give a holistic data science process? The answer is through cloud ELT tools like LIKE.TG Data. Using ELT (Extract, Load, Transform) we can extract data from data sources, load it into the data infrastructure, and then transform it in the way that is required. ELT tools act as the strong bridge between data sources and destinations, allowing seamless flow and control of data to enable advanced data science applications like AI, BI or ML. It allows data engineers to focus on the intricacies of these projects rather than the mundane building and maintenance activities involved with building data pipelines. Cloud ELT providers allow you to have a lean analytics model (Lean Analytics, Yoskovitz and Kroll), treating analytics like a process, allowing iterations on ideas, as they allow businesses to scale according to their data volumes. Dashboards demos can be built and validated by the stakeholders. Then gaps can be identified, and the dashboards can be launched into production using new ingested data. Advancements happen within days instead of months, allowing an amazing speed of execution. Hence, the value of such tools increases as an organization grows. These tools also help with access, governance and control, solving many of the basic blocks required for advanced data analytics and enabling accelerated success. Details About Partners About LIKE.TG Data: LIKE.TG Data is an intuitive data pipeline platform that modern data analytics teams across 40+ countries rely on to fuel timely analytics and data-driven decisions. LIKE.TG Data helps them reliably and effortlessly sync data from 150+ SaaS apps and other data sources to any cloud warehouse or data lake and turn it analytics-ready through intuitive models and workflows. Learn more about LIKE.TG Data here: www.like.tg. About Danu Consulting: Danu Consulting is a consulting firm specializing in big data and analytics strategies to support the growth and profitability of companies. Its solutions include data migration to the cloud, creation of BI dashboards, development of machine learning and AI algorithms, all adapted to the unique needs of each client. With over 15 clients and 50+ projects, Danu Consulting has the solution your company needs. Lear more about Danu Consulting at www.danucg.com

Remote work is already a haven for employees and freelancers. As a manager, you’ll also see the flexibility of working from home and outsourcing their business to anywhere from one to a large group of remote workers. So, when you have a remote team of workers, you must manage them in ways that make production consistent and effective, without being afflicted with missed deadlines and miscommunication. Here’s our guide to manage remote workers, with seven ways to effectively manage your remote workforce, with the tools to manage remote employees, so that your business can still thrive on these terms. 1. Encourage Company Culture “Working remotely doesn’t mean that there can’t be any company culture,” says Joseph Rouse, an HR at Paperfellows and Australianhelp. “Your workplace culture should have values, at the very least. Having company pride in how you do business and how you communicate allow you to have a good experience with your remote workers. And with that said, your workers should value those same values, and stay in touch with their community as often as possible.” Of course, when remote team members are working across time zones, employees feel isolated easily, and company culture becomes a difficult thing to encourage. But with regular team meetings via video conferencing, you’ll find you can easily build trust and employee engagement with your remote workforce. 2. Give Workers The Right Tools To get production going, your workers need the right tools to do the work: access to files, tasks, information, etc. So, make sure that they have access, and that they’re up to date on news, updates, and deadlines, so that they can all get work done at a decent pace. You will need to make sure your employees have access to all the software needed to do their work—word processing software, team scheduling and productivity software, and conference call software, especially important for making remote workers feel part of the rest of the team. If this software is free, you mainly only need to provide a download and installation guide for your remote team members. But if the software has a paid license or needs a company login, be aware that your remote workforce may find it more difficult to set this up than people working in-house, especially if they cannot easily get in contact with the IT department, or do not have access to the company IP address and portal. 3. Utilize Video Chat One of the best tools to manage remote employees, for managing remote workers training or otherwise, there are other ways to communicate with your workers, rather than just texting them. Communication via video chat is a fantastic way to break out of the monotony of standard team meetings, and helps people working from home feel like they are part of the rest of the team during team meetings. If you are sure to schedule your conference call well in advance, paying attention to your remote workforce’s varying time zones, chat services like Skype, Google Hangouts, and Zoom allow you to communicate with your workforce whenever you’d like. And if connectivity should fail, you ought to fix it right away. Or if your employees experience downtime, see if you can help cover the cost for better Internet or more bandwidth for them, or work with them to see where they can take calls without any issues. 4. Communicate And Set Goals It’s imperative to communicate clearly with your employees. Do not leave a thing to chance. Enquire and query with your employees to get the discussion going. Also, allow your workers to ask you their requests and queries of their own. In addition, make sure that you’re stating your expectations to them as clearly as possible. Have goals and timelines that you want to see get accomplished, and hold people accountable for the work that they’re doing. As above, accountability and measuring productivity are easier when you have the tools and software in place to aid them. But you should agree with your employees whether they are getting paid by the hour, for example, or another way of measuring it. And you should check in with your remote workforce now and then, especially in the middle of a larger project, to see how things are coming along, what stage your employees are at, and see if this corroborates with the goal. This communication will help build trust. 5. Longer One-On-Ones This is something found in any managing remote employees book. One of the most important tools to manage remote employees, as you allow more communication into the workflow, when you manage remote workers, make sure you have time to have one-on-ones with anyone who needs it. You can update your availability for your “open-door policy” on services like Skype, Slack, etc., so that your employees will know when you’re available and when you’re not. And, when you do have one-on-one time with your employees, give them a full hour every week to discuss anything that needs to be discussed. Here are some good questions to ask, when doing these one-on-ones: What’s your favorite part about working remotely? How would you describe your daily routine for when you work? Do you believe that you are making a difference in our team? Why do you or don’t you believe this? Are the tools that you use functioning effectively for you? If not, how can our software and equipment be improved? You visit the office a certain number of times a year. Is the visit amount extortionate, inadequate, or perfect? How can I better support you on this remote venture? 6. Respect Your Workers “When it comes to how to manage remote employees successfully, a lot of companies take them for granted,” says Xavier Samuels, a project manager at Bigassignments and Boomessays. But one of his best tips for managing remote workers is, “In fact, it can be hard to keep remote workers, if you work them too hard, or don’t express your appreciation for them. So, if you’re lucky enough to find remote workers who do their job well all the time, then it’s fair to treat them with respect and pay them fairly for their work. Like regular employees, remote workers appreciate the positive feedback.” Throughout the contract, if you communicate effectively with people working remotely, like using instant messaging for shorter, more casual messages, and respect your remote employees’ time zones when you do, but stick to sending longer messages and briefs through email, people working remotely will look forward to working with you in the future. 7. Get Together In-Person At Least Annually What is there to dislike about a face to face team meeting every once in a while? Whether you host a luncheon or a managing remote employees seminar, it’s fun to see your workers in person rather than through video conferencing, and connect with them on a social level. Employees feel more connected. As you have these social outings annually (if not a few times a year), you can all talk about the company or team’s future together. When it comes to how to manage remote employees successfully, this is imperative. It will build employee engagement and trust among all team members, not just the group who works from home. Conclusion By following these simple steps, you’ll create a remote team that not only works with you, but is also appreciative of what you have to offer them. About author: Molly Crockett is a marketing and business expert for Ukwritings and Essayroo. She gives managers tips on how to better optimize their business practices. She’s also a writing teacher at Academized, where she teaches young people how to develop their writing and research skills.

It can be tough to onboard new employees, particularly if they’re remote. They can't just pop into the office for a meeting, and you can't always quickly bring them up to speed on all the company's goings-on. But with a little effort, you can make the process smooth for both the employee and your team. Several tools and technologies can help make the onboarding process easier for you and your new employee. For example, video conferencing software like Skype or Zoom can be used for virtual training and tours. And there are many online tools, such as Google Drive, that can be used for collaboration and sharing documents. In addition to taking advantage of these tools, you can make the onboarding process smoother and more efficient with the following guide, designed to help you get started. 1. Have a remote work policy If you're going to be onboarding remote employees regularly, it's important to have a remote work policy in place. This should outline your expectations for how employees will conduct themselves while working remotely and what you expect from them regarding communication and collaboration. Having a clear policy will help make the onboarding process smoother and ensure everyone is on the same page from the start. And it starts in the hiring process: In your application, ask candidates what aspects of remote work appeal to them. Ask them to describe their preferred working style, communication style, and technical skills. If a candidate responds that they do not enjoy the isolation of remote work, you may want to move on. 2. Send equipment and a welcome package beforehand Few things will frustrate both you and your new employee more than having them spend the first few working days ironing out technical difficulties. So, ahead of time, order all necessary hardware they’ll need and have it delivered to their home. Here are some items to consider, depending on the requirements of the position: Laptop or desktop computer Monitor Keyboard and mouse Headset or earphones Webcam Printer/scanner External hard drive or USB drives Phone or VOIP equipment Charging accessories and power strips When it comes to software, if what employees need isn’t cloud-based, ensure it's pre-installed on their device. Include passwords, usernames, and any other security information needed so remote hires can easily get up and running. Suppose you are working on many different online marketing tools. In that case, putting together a list of recommended tools for your new employee may be helpful. Or if you’re working with a project management tool, ensure that their accounts are already set up in advance, and they’ll have access from day one. Additionally, making remote employees feel welcome can help embed them into your organization and feel like they’re part of the team. One idea is to send a welcome package with things like: Welcome letter from the CEO or team leader Company swag such as T-shirts, mugs, notebooks, or pens Employee handbook Snack box with an assortment of treats and snacks to enjoy while working Tech accessories like USB flash drive, ergonomic mouse pads, or laptop stands Gift cards for coffee or online retailers Virtual event pass to an upcoming online workshop or team-building event Desk plant to brighten up their home office space 3. Be prepared with virtual onboarding materials Gather all your onboarding materials and make digital copies as part of your remote onboarding program. You may also want to mail them a physical copy along with their welcome package. Here’s a list of things you’ll want to include: For all employees: Mission, vision, and values Organizational charts Employee directories Communication procedures (how and when to use email, video calls, and chat) Tutorials for commonly used tools Security standards Templates for standard documents (presentations, email invitations, sales follow-up emails, etc.) For marketing: Lead qualification criteria Content style guide Blog and SEO best practices For sales: CRM contact information standards How to order business cards Travel and expense procedures For software developers: How to set up a development environment Codebase Development processes Architecture standards You can help employees track their progress by making videos of everything that they need to learn in modules. Or you can set up a video call so that they’re able to ask questions on anything they don’t understand and managers can easily answer them on the spot. Creating these learning modules might be a bit difficult, but you can contract a Professional Employer Organization (PEO) to create learning modules for any job position. 4. Leverage existing technology and tools Use video conferencing or online team meetings to ensure a smooth transition. Companies onboarding remote employees can greatly benefit from leveraging technology and platforms such as Zoom for virtual meetings, Slack for communication, Asana for project management, LIKE.TG for HR software, and Microsoft Teams for collaboration and document sharing. For example, you can use Microsoft Teams to welcome your new team member, outline their responsibilities, and offer support. And some apps, such as Donut, allow employees to chat with a company representative through the internet. Onboarded employees can also receive useful information about company resources, such as knowledge bases, training software portals, and cheat sheets 5. Let new hires know what to expect Working from home can be a brand new experience to most. Ensure everyone understands what's expected of them during the remote onboarding process. What information do they need? Whom will they be meeting with? What tools do they need to be successful? You can avoid confusion and frustration later on by getting everyone on the same page from the start. This is also a good time to set expectations for communication. Will you use email, Slack, video conferencing, or a combination of all three? Let your new employee know how you prefer to communicate and the best way to reach you. 6. Set up a dedicated onboarding space If possible, set up a dedicated space for their remote onboarding process. This gives them a place to go where they can find all the information they need in one spot. It can be as simple as a folder in your company's shared drive or an intranet page with links to all the relevant documents. Having a dedicated space also makes it easy for you to keep track of your new employee's progress. You can quickly see what they've read and what still needs to be covered. As your business grows, it might be a good idea to consider tools to help during this process. LIKE.TG’s employee Onboard platform can help to automate a lot of the repetitive tasks that build up as you get the hang of remote onboarding. Download Our Free New Employee Orientation Checklist! Download Now 7. Use self-onboarding checklists Self-onboarding checklists are an effective tool for streamlining the onboarding process of remote employees, ensuring they complete all necessary steps. These checklists should include tasks like setting up company email accounts, completing required paperwork, reviewing company policies and procedures, and accessing necessary software and tools. Additionally, the checklist can guide new hires through introductory training modules, schedule their first team meetings, and prompt them to set up virtual meet-and-greets with key team members. 8. Introduce them to the team members One of the challenges of remote work is feeling like you're part of the team. So take some time to introduce your new employee to everyone on the team, even if it's just through a quick email or video call. If possible, set up regular virtual coffee chats or happy hours so they can get to know their colleagues in a more informal setting. You can also give them the company’s organization chart so that they do not have a hard time trying to remember who is who. 9. Give them a virtual tour If your company has a physical office, give your new employee a virtual tour so they can see where their team members work and what the space looks like. If you don't have an office, you can still give them a tour of your company's website, intranet, or social media channels. Make them feel like they're part of the team by showing them around and introducing them to everyone they'll be working with, even if it's just virtually. Always make an effort to have your camera on to create a warm face to face feeling when giving the tour. Click Trough an Onboarding Process Click through our interactive demo to see how LIKE.TG makes onboarding easy. Try It Now 10. Assign a buddy One way to help your new employee feel welcome is to assign them a buddy. This should be someone who's been with the company for a while and knows the ropes. They can answer your new employee's questions and help them feel comfortable in their new role. A workplace mentor can provide initial guidance and help eliminate the anxiety many new remote employees experience. Moreover, use video coffee chats and other ice-breaker activities to break the ice. 11. Provide training and resources As part of the onboarding program, provide your new employee with all the training sessions and resources they need to be successful in their role. An onboarding training session should entail a thorough introduction to the company culture, key policies, and job-specific skills, along with interactive elements like QA sessions, practical exercises, and opportunities to meet and engage with team members and key department leads. The goal is to set them up for success by providing everything they need to hit the ground running and make sure you’re both on the same page once their onboarding plan is complete. 12. Encourage communication and feedback Working remotely makes it easy to feel like you're out of sight and out of mind. To avoid this, schedule regular check-ins with your new employee. This gives you a chance to see how they're doing, answer any questions they may have, and give feedback on their progress. You should also be open to getting feedback from your employees. This helps you figure out problems and come up with solutions. Feedback will show you places where your new employees have problems. There are different ways in which you can collect feedback from your new employees. You can use surveys, meetings, or performance tracking software. Check-ins also allow your new employee to bring up any concerns or issues they may be having. By addressing these early on, you can help them feel more comfortable in their role and prevent any potential problems down the road. You can also ask them to turn on their video during conference meetings to make them settle in properly so you become aware of their onboarding experience. Since employees are not able to meet face to face, online meetings and daily communication are essential in team building and creating healthy remote team relationships. 13. Give them room to grow Finally, remember that your new employee is still learning and growing into their role. They may make some mistakes along the way, but that's okay. What's important is that you give them the space to learn and grow.Encourage them to ask questions, try new things, and take risks. This will help them become even more successful in their role and feel like they're truly part of the team. “As soon as we saw LIKE.TG’s Onboard demo, we knew this was the perfect solution for us. We loved that it was extremely simple and powerful out of the box, but that we could customize it with advanced capabilities to make it work in our company setting.” Elisa Garn Vice President, HR and Talent Christopherson Business Travel Learn More About Author: This article is written by our marketing team at LIKE.TG. LIKE.TG is dedicated to providing powerful solutions for your HR teams and creating an exceptional employee experience. Our aim is to help your company improve employee engagement, onboarding, and to save you valuable time!

Months into the Coronavirus pandemic, organizations are continuing to feel the need for learning additional, seamless integrations with other systems related to hiring/onboarding, performance management, and employee communications, engagement and rewards. Our integration with ADP and the inclusion of our Onboard and Workmates solutions in the ADP Marketplace is proof of this power of integration. If you’re not aware, the ADP Marketplace gives HR teams secure, turnkey apps and technology to develop innovative new approaches to workforce management. LIKE.TG’s integration with ADP now means that any HR user can log in to Onboard or Workmates with their ADP credentials or choose to import all of their workforce data with one, easy click. Additionally, the ADP Marketplace lets any company (or HR department) put its workforce data to use well beyond typical use with ADP’s payroll, taxes or other functionality. For example, software developers can now create innovative new apps to make even better use of workforce data. That’s why LIKE.TG has always prioritized integration with ADP applications, specifically our Onboard and Workmates solutions. For example, as the Coronavirus pandemic continues to complicate the workplace with issues related to hiring to team collaboration and performance management, LIKE.TG clients are able to see the benefits of automation and communication that comes from ADP integration. Michael HawkinsFranchise Owner of Interim HealthCare SLC “Interim Healthcare SLC needed HR technology, and we’re pleased with the results we’ve gained from LIKE.TG’s solutions for recruiting, onboarding, and employee engagement. Yet it’s an opportunity for all Interim franchises. It would be so great if each franchise owner could implement similar solutions to replace legacy systems that might not work as well as they should.” Let’s take a closer look at the HR-Cloud-ADP integrations and the many benefits they can provide. Seamless remote onboarding The hiring and onboarding process involves multiple groups and has strict legal requirements. Our seamless integration with ADP ensures a reliable data flow to and from LIKE.TG, including employee profile information, direct deposit and more. Forms can be built automatically using an intuitive drag-and-drop interface, which means many onboarding processes can be updated and automated “on the fly,” to support remote workers and HR teams. Exceptional employee experiences Convenience has always been key to attracting and retaining top talent, and the pandemic hasn’t changed that. In fact, new employees will appreciate HR organizations that demonstrate a commitment to employee needs at this time and have worked to modernize the experience. Starting a new job during this time is likely to be stressful, and being able to offer an efficient and even appealing onboarding experience matters a great deal. Integration with ADP ensures that an employee’s first experience is stress-free with fewer headaches and process snafus. Improved HR efficiency Integration between Onboard and ADP improves HR efficiency in two important ways: Employee self-service puts the impetus for workforce acclimation into the hands of your new hires and makes them confident contributors on day one. It also gives your HR team the tools to create curated task workflows that guide employees through a compliant automated process that you define and manage effectively via an online dashboard. Effective employee communication With Workmates, organizations can create a digital town square of sorts, where ideas and needs are communicated between HR, employees, and management. Workmates enable you to implement new company initiatives across a remote workforce simultaneously and even receive immediate feedback. Workmates also increase overall engagement, as new hires will find themselves comfortably absorbed into workplace conversation by using Workmates and the accompanying org chart as functional company directories. What’s more, the integration with ADP ensures that valuable employee details such as anniversaries and birthdays are readily available to HR and managers, putting them in a position to recognize employee dedication remotely. LIKE.TG-ADP integration lets you do more Like many organizations, you may be preparing to make a long-term commitment to remote workforce management. Knowing the challenges that may already exist with IT support and integration planning, there is value in having a partner that has already addressed these challenges. With LIKE.TG and ADP, you can be certain that onboarding and workplace efficiency are challenges that are already addressed from day one.

People come, and they go—especially at work! Sometimes it’s due to involuntary turnover like layoffs, while some employees resign or take another job. But it’s much better if the people you hire stay for the long term. Why? Because it costs an organization 6 to 9 months of an employee’s salary to replace them. For example, it will cost you $30,000 to $45,000 to hire and train a replacement for a $60,000 salary employee. Curious how much it will cost you? Try our employee turnover calculator. High employee retention also helps foster trust and engagement between employees and employers, which is very important for maintaining a motivated and engaged workforce with a strong sense of loyalty. Simply put, businesses can't afford not to care about the happiness of their employees. First, you need to understand the factors that significantly affect it. Then, if you find any gaps in your processes, improve them now rather than regret inaction later. Below we've listed actionable strategies to address the factors influencing employee retention. 1. Competitive Wages and Benefits Everyone wants a job that pays them well and offers them great benefits. And employees feel like a company cares about them when they receive competitive benefits. So make sure that your employees are fairly compensated for their work. This can include offering a competitive base salary, bonuses, and other incentives such as paid vacation, technology reimbursement, health insurance, 401K plans, assistance with educational expenses through programs like Pell Grants, tuition reimbursement, etc. Offering these benefits will not only make your job roles more attractive but will also help you create a productive workforce. It will also help you attract and retain top talents in your company. As a result, your business outcomes will improve, and your conversions will see a major boost. Consider offering benefits that grow in value over time or get better with tenure to help prevent employees from leaving for greener pastures. That way, they don't have to start from scratch elsewhere. What You Can Do: Now’s the best time to review and assess the current benefits you offer to your employees. If you currently don’t have benefits designed to improve over time, some examples are: Bonus structures that increase with tenure Granting more paid time off for employees with greater lengths of service Stock options with vesting periods 401(k) matching contributions with vesting periods For newer employees, you can look into enhancing your current health benefits. Alternatively, you can even offer unique benefits such as continuing education, discounts on products or services, laundry services, and free food. 2. Onboarding and Training Do your employees leave within the first six months? Are they taking on lateral responsibilities at other businesses? If you are having problems with onboarding new hires, then you have short-term retention problems that you need to address ASAP. So, have you reviewed or upgraded your recruitment processes recently? The way you recruit, onboard, and train employees has an impact on employee turnover. Failing to address retention at this stage might result in losing the best employees immediately. What You Can Do: Most onboarding concerns come from inaccurate job depictions during the interview stage. Employees are less likely to stick around if you aren't clear. To address this, be honest. Give them the obligations that come with the role they are applying for. Make sure that all candidates are clear on expectations from the start. This will increase the likelihood that they will stay with your organization. 3. People and Culture Humans are social creatures. Above all things, we want to create an emotional connection with the people around us. That's why there's an increasing average number of employees who want to be in workplaces where they feel like they belong. Creating a favorable work environment will work wonders for you and your employees! An enhanced company culture will reduce time spent worrying about productivity, employee engagement, motivation, and retention. What You Can Do: Part of human resources’s responsibilities is to carefully examine the company's values and consider how you can convey them to your staff in a tangible way. If your organization emphasizes creativity and flexibility, engage with your team members to create flexible working arrangements that meet the demands of both the individual and the company. Here are some ways to go about this: If you value transparency, make it easy for employees to access relevant documentation and approach dispute resolution with empathy and honesty. If respect is a corporate value, offer diversity and inclusion training programs to tackle unconscious biases. You can also demonstrate that you care by asking for and acting on employee feedback. 4. Work Schedules The pandemic has forced a lot of companies to transition into a permanent work-from-home structure. And employees love the fact that they don’t necessarily need to be present in the office and can work on flexible schedules. It helps them save time and money from the daily commute and gives them the ability to utilize their time more productively. By offering a flexible work schedule, you allow your employees to have more control over their time. This is a great way to offer them a better work-life balance. It also shows your employees that you trust them and value their time and well-being. This, again, is very important for creating a strong work culture. What You Can Do: You can offer flexible work schedules in a variety of ways. For example, you can allow them to choose their start and end times, offer compressed work weeks, or give them the option to work permanently from home. Remote working software could help you manage your remote employees quite easily. 5. Recognition No one appreciates feeling unappreciated. Employee turnover can be influenced by a lack of recognition, which can reflect a poor management style. It will inevitably lead to employees seeking the attention they deserve elsewhere. And while a lack of acknowledgment may not cost you your best employees immediately, failing to address it will result in low morale and decreased productivity. Create a company culture where employees’ hard work is recognized and appreciated. This makes working for the company enjoyable and rewarding. Encourage employees to participate in team-building activities and celebrate successes. You should also acknowledge and reward them for their accomplishments and hard work. This can be done through bonuses, awards, or public recognition. Sometimes even a simple “thank you” or “well done” can go a long way in motivating employees and making them feel appreciated. What You Can Do: Find ways for employees to feel heard and recognized. Everyone loves receiving positive feedback, and employee retention is heavily influenced by leadership's active listening and recognition of employee successes. In practice, this will look something like: Implementing a reward system to recognize small and big wins Providing yearly performance evaluation Training management personnel to provide positive reinforcement Scheduling one-on-one sessions between employees and their supervisors regularly Making sure that they are paid on a salary that is equal to the role they perform 6. Work-Life Balance Employees want to have time with their families, hobbies, and other activities outside of work. More and more employees want flexible work schedules that allow them to care for their professional and personal lives. So, make sure that there is no room for employee burnout in your workplace. Burnout happens when individuals feel out of control or under a lot of daily stress. Not only will this decrease their productivity, but it will also affect their mental and physical health. What You Can Do: To improve your employees’ work-life balance, every now and then, ask yourself the following questions: Do you regularly demand or expect employees to work after hours or on weekends? Is a 50-hour workweek really “normal”? Do you offer employees the tools, resources, and technologies they need to succeed? Let your employees have time for themselves and their families. Rather than the traditional 9-to-5 model, consider offering remote working options. You can also implement flexible working arrangements. This will allow employees the flexibility to manage their working hours. Plus, they can work from home if they need to! Encourage employees to set limits and use vacation time. But, if late nights can’t be avoided, consider compensating them with more time off. 7. Communication Employees appreciate workplaces where they can freely voice their opinions, thoughts, ideas, and concerns without judgment or fear of retribution. So always encourage your employees to communicate with you openly. Let them know that their opinions, ideas, thoughts, etc., are important to you and that you value them thoroughly. Organizations that encourage open communication have a better work environment and have employees that are better engaged. They are also capable of fostering trust and respect between management and employees. What You Can Do: There are various ways to encourage open communication in your organization. For example, you can establish regular channels for employee feedback, such as surveys, focus groups, or one-on-one meetings. You can also conduct open forums to discuss challenges, successes, and failures. You should also have an open-door policy where employees feel comfortable approaching the management with their questions and concerns. 8. Career Development Your employees may sense a lack of growth opportunities within the company. Nobody enjoys completing the same monotonous tasks every day, especially when the scope of the work isn't expanding. Employees feel insecure about their jobs when there is little room for advancement. This is when they begin seeking other opportunities. Give your employees the opportunity to learn and grow. The idea is to create a ladder of career advancement where your employees can truly experience growth. This is very important to keep your workforce motivated and improve their efficiency as well. Investing in your people shows that you care about their professional development and potential for advancement to more senior roles within the organization. This creates a good cycle of belonging, motivation, productivity, and retention. What You Can Do: Does your company have an employee retention program yet? If not, your employees are likely to look elsewhere. They’ll search for companies that recognize their contributions more quickly and offer prospects for growth. Try offering on-the-job training, giving them access to learning resources, offering education programs, mentorship programs, etc. Sometimes it can be a good idea to encourage your employees to attend industry conferences and seminars as part of their learning process. Another way to make that happen is to create job rotation and internal promotion opportunities. It is your responsibility to lay down a clear-cut career path for your employees. For their continuous growth, you can provide top performers with training programs, seminars, and conferences, or in-company apprenticeships or mentoring. Alternatively, you can also connect them to online courses. Happy Employees Stay Longer Regularly review, reassess, and reinvent your employee retention strategy. Ensure that you meet your employees' needs. Remember, retention will come easy if you boost employee happiness in every process you implement throughout the workplace. Try Workmates Interactive Demo Click through it yourself withinteractive demo. Try It Now About Author: This article is written by our marketing team atLIKE.TG. LIKE.TG is dedicated to providing powerful solutions for your HR teams and creating an exceptional employee experience. Our aim is to help your company improve employee engagement, onboarding, and to save you valuable time!

6 Remote Work Challenges for HR This blog article focuses on 6 challenges HR faces in managing remote teams. To learn even more, please download the LIKE.TG eBook, “A Better Way to Communicate, Engage, and Recognize and Reward Remote Teams” today. Even before the coronavirus and its work-at-home mandate, remote work was an accepted best practice. Research showed that approximately 60% of employees were working remotely and a full 30% were fully remote. The COVID-19 crisis has pushed these numbers even higher and not just for the short term: early feedback shows that many employees hope to keep working from home in the “new normal.” Yet many companies continue to use traditional management methods—those designed for office and on-site workers—that are not as effective as they should be for a remote workforce. Such an approach leaves employees feeling disconnected, creates HR inefficiencies, and can even have an adverse effect on the business itself. Let’s take a closer look at six challenges most HR teams face when attempting to manage remote teams. #1: Interviewing, hiring, and onboarding Recruiters and HR teams have always had a hard time making the “perfect hire,” even when they were able to conduct face-to-face interviews. Yet the challenge becomes even more difficult for HR teams attempting to find, interview, and hire candidates from a distance. Where telephone calls are good, video conferences are better—helpful to see and “meet” the candidate and observe important non-verbal cues. Yet even when candidates are hired, remote onboarding can be a real challenge for HR departments, especially since it may be a new concept for many HR professionals. To help, here’s a quick checklist to improve remote onboarding processes: Create an individual onboarding plan with check ins at the right time frames (e.g., 30, 60, or 90 days). These should clearly communicate objectives and give the opportunity for questions. Digitize all documentation including forms, documents, guidelines, tax documents, and company-specific information, such as employee handbooks, employee directories, and more. LIKE.TG’s Onboard solution lets users create personalized portals so new hires can review, complete, and send all of this information before they even start. Make sure new hires are set up with the right applications, systems access, badges, equipment, and other tools they might need. This can be done by creating a customized checklist that includes the right company personnel. For example, letting facilities know the new employee needs a new badge before they start, helps make sure nothing slips through the cracks. Schedule and conduct remote orientation sessions for new hires. This can serve as a differentiator—imagine how many other companies skip this step—and can go a long way to engaging employees, starting on day one. Introduce remote team members using a video call, a virtual coffee break, or other event. This will help all team members “put a name to a face” and encourage collaboration. Make sure new employees have all the other information they might need, including training, employee profiles, employee directories, contact information, and more. #2: Focus on employee engagement We all know employee engagement is important, both for the worker and the business itself. For example, research has shown that the teams with a high level of engagement demonstrated 21% higher profitability than those with lower levels for employee engagement. However, a drop in remote employees’ engagement (along with morale, productivity, and even retention) may occur if HR teams don’t focus on it. Engaging all workers--no matter where they work--is important to make them feel informed, valued, and part of the larger team. Related resource: To learn even more about managing remote teams and keeping employees working at home highly engaged, download our eBook, “A Better Way to Communicate, Engage, Recognize and Reward Remote Teams” now. HR solutions, such as employee communications platforms, can be an effective way to help HR teams connect with remote teams. For example, these solutions can: Give them fast, easy access to critical information Use mobile apps to give them the same information using their mobile device Enable remote teams to communicate using email, chat, calling, or video calls Let users post pictures, videos, GIFs, and more to make collaboration fun Let any worker recognize a peer’s hard work or efforts, and earn points for gifts #3: Improve remote workforce communications According to Forbes, employees spend approximately 2.5 hours a day reading, writing, and responding to emails--not ideal use of their time. Additionally, research has shown that poor communication now costs companies a total of $37 billion each and every year. The situation--and the challenge--only become worse when attempting to communicate with remote teams. Modern HR solutions now offer a better way to communicate with remote teams, all in one platform. For example, our Workmates platform gives each employee a centralized, personalized newsfeed and group channels, so they will always receive the information they need. Or, if they have any questions, they can instantly connect to a manager or team member using chat, email, text, or call. You could also engage in remote team-building games. #4. Training and development Did you know that 67% of remote employees wish they had more access to work-related training? Unfortunately, the COVID-19 crisis has only made this situation worse. Sharing information and improving the skills of remote workers not only helps them do their jobs well but it also contributes to personal and professional development. HR teams can provide remote employees with access to training articles, videos, online courses, mentoring opportunities, and more. This can all be delivered as part of a performance management solution, or with an integration to a learning management tool. #5: Transform your culture Sometimes it’s hard to connect with remote employees and make them understand, and feel like they’re part of, your company culture. Today, effective employee communication and engagement platforms can give all employees access to all the information they need. For example, these solutions include content management systems (CMS), employee advocacy information, and even recognition and rewards capabilities. All of this helps any company weave remote workers into their culture, and even better, transform it into culture focused on excellence. #6: Provide access to information Research shows that remote workers spend 19% more time searching for information they need or otherwise attempting to navigate access issues. Again, this is not ideal use of their time. One way to overcome this challenge is through the use of a mobile app. The benefit is to give remote workers fast, easy access to all the information they need, yet deliver it in a mobile app that is identical to what they would expect to find using desktop-based software. This way, employees working from home can access the CMS, their own customized content channels, HR records, employee directories, and more. Implement a winning strategy to manage remote teams Managing remote teams is now the new normal, even beyond the COVID-19 crisis. However, HR teams may still face a number of challenges as they attempt to hire, onboard, attract, and retain remote employees. Today, innovative HR teams are embracing HR software, especially remote onboarding and employee engagement platforms to improve the way they connect, communicate, and collaborate with remote teams. To learn more, download our eBook “A Better Way to Communicate, Engage, Recognize and Reward Remote Teams” today! About author: Alexey Kutsenko is the Head of the Marketing and Employer Brand department of DDI Development company. He is passionate about HR processes in the company as far as they are cornerstones of the company’s general success.

“A great overview by Gigster CEO Chris Keene on how the gig economy is transforming work in the enterprise by making it more customer-centric and high performance. Don’t miss the stories of companies actually doing this.” Dion Hintchcliffe, Vice President and Principal Analyst, Constellation Research As software continues to eat the world, companies must find ways to build more innovative teams. For these companies, The Future of Work is here today. How companies reshape the way they engage digital talent will have a huge impact on their ability to innovate. Here are six ways digital leaders are creating The Future of Work now. 1. Remote workers and work from home teams are the new normal The best talent is not always located where you are. Even within a company, silos can prevent the right people from working on the most critical innovation projects. Making it possible for team members to work remotely is the only way to liberate talent across the company. This is also the only way to source critical skill gaps that are not available inside the company, like AI/ML engineers. Supporting distributed teams calls for adopting a common set of processes and collaboration tools, including Slack, Github and Jira. 2. Hybrid teams beat monoculture teams Innovation requires a diverse mix of talent. The most innovative teams blend in-house employees who have industry context with expert global talent who have advanced technical skills. This is the opposite of the traditional systems integration model that outsources innovation. Hybrid teams also have the value of helping companies build in-house skills by working side by side with expert freelance talent. 3. Elastic Staffing beats fixed staffing Many companies adopt agile team processes but staff teams inefficiently — they follow a fixed staffing model that allocates each role as a full-time position for the duration of a project. This is only half agile. Because it makes experimentation too expensive to try, and reduces employee mobility and satisfaction. Compare this to Elastic Staffing, which allocates resources based on the workload for each project phase. For example, developers can join a project after the detailed design is complete, and technical architects may only be needed part time. Elastic Staffing can reduce the total hours to deliver innovative products by over 50%. 4. Employees want to be treated more like freelancers Top employees want the freedom and flexibility to choose how they work and what they work on. This doesn’t have to require radical organizational changes. For example, applying the Google 20% rule, where workers can choose their own projects 20% of the time, can boost morale and build skills. This lets more senior employees peer review deliverables from other projects to reduce risk. It also helps more junior employees grow by being exposed to new business and technology challenges. 5. Freelancers want to be treated more like employees Top freelancers want stability and work benefits without sacrificing their flexibility. Companies that learn how to work effectively with freelancers will have their pick of the best global talent. This includes setting up projects for success with distributed teams, incorporating advanced technologies, and providing predictability for freelancers that helps them plan effectively. In California, laws like AB5 are beginning to mandate providing more benefits for freelance workers, and putting them more on par with the benefits that full-time employees receive. 6. Automated team and talent assessments are here Technology will ultimately reshape jobs, but today, technology is reshaping talent ratings. Every collaborative tool – from Slack, to Jira, to Github – has open APIs that can automatically collect data about the productivity and quality of work being produced by people and teams. Tools like Pinpoint can collect this data. Applying analytics to this data enables the creation of “karma scores” by person and by team, to provide objective and trusted evaluation of skills. Examples of The Future of Work today A global telco created hybrid teams that mix in-house staff with top global experts to accelerate their machine learning and predictive analytics initiatives. They manage these distributed teams following Silicon Valley best practices and have been able to deliver new applications that leverage AI up to twice as fast as traditional in-house development teams. One of the world’s largest digital agencies created an entirely new digital transformation business unit using hybrid teams that blend employees and global freelance talent. This approach enabled them to onboard over 100 engineers in less than six months, with minimal recruiting costs. Summary The Future of Work describes a cultural shift that companies must adopt to grow their innovation capacity. The pace of those changes is accelerating as more companies adopt new work from home rules that support remote workers. Companies that embrace a Silicon Valley-style culture of innovation can become more customer-centered, more able to tap new talent pools, and to dramatically reduce risk. About the author: Chris Keene: As CEO, Chris drives Gigster’s vision to de-risk digital innovation. Chris was previously VP Cloud for VMware, where he led the $400 million Pivotal spinout. Chris also founded and took public Persistence Software (NASDAQ:PRSW) This article is originally posted on Gigster.com

Managing people—especially in an organization — requires a level of social, emotional, and cognitive fortitude and intelligence that not everyone can boast. This is why people-focused roles, like being a business owner or working in a company's HR department can be overwhelming. Every step you take has to be intentional and has to work towards the good of the company. But in the same breath, you have to also keep the peace among staff members while fostering harmony and cooperation. When engaging with a member of staff one-on-one, it's relatively easy to achieve this. But what happens during employee meetings when every member of staff is there at the same time? With all the different ideas and personality types in the room, keeping the peace or even achieving any serious objective can prove futile. Does this mean that organizing productive staff meetings is a pipe dream? Thankfully, the answer is no. You can hold employee meetings that your staff will look forward to. We've outlined a few simple tips to help make your next employee meeting both meaningful and memorable — in a right way. Whether you're holding the meeting in a physical room or via teleconferencing, you can take these staff meeting best practices to the bank, regardless of the business or industry you work in. 1. Keep The Discussions Within the Meeting's Scope One of the most apparent traits of productive staff meetings is that all discussions stay focused. No one wants a meeting where the convener just rambles on about anything and everything concerning the company. Every meeting should have a plan that should be strictly adhered to. This is not the time to address a dispute between two employees, openly rebuke an employee's shortcomings, or talk about the misuse of office equipment. Do this, and all you'll be achieving is a lower staff loyalty score. A neat hack would be to prepare the agenda a few days before the meeting and hand it out to your employees. This way, your staff knows what the meeting is going to be about and have a few days to prepare themselves for it. This makes for a more agreeable audience when the day comes around. All your employees have got work to do to help grow your company, so keep the meetings short and focused so everyone can return to work. 2. Encourage Employee Input People tend to learn better when they talk less and listen more, an approach that should be taken by every business owner and manager when dealing with employees. The people that work in your establishment are much more than the roles they fulfill. Some of them have vast personal or professional experiences from their life before and outside your company. You may be the one at the helm of affairs, and you may know the objectives of the company more than anyone else. However, having more experienced heads to brainstorm with never hurt anyone. When you let your employees give their two cents, it opens you up to a world of possibilities as to how to deal with the current problem. It may be a novel idea or a reminder of something you've heard before. Either way, it helps the problem-solving process. Not to mention that it is suitable for the workplace atmosphere when employees know that their boss is willing to listen to their problems and their ideas and act on them. It gives them a sense of belonging and worth and increases the company's loyalty score. This approach to problem-solving during meetings significantly reduces the possibility of the leaders coming to a one-sided and subjective decision that does not work for everybody. 3. Keep Your Focus Only On The Stuff That Matters The best way to deal with the wrongness of a situation is not to expound on how wrong it is, but to focus on how to make it right. This is, unfortunately, a mistake many business leaders still make when something goes wrong in the company. Say a deal goes bad, they call for a staff meeting and dive into an investigation to find out how things went so wrong. They spend all the time berating the employees and attacking all the decisions they made leading up to the bad deal. Most of the time, this achieves nothing but dampens the spirits of everyone in the meeting. A better approach to have an effective staff meeting would be to focus on how to salvage the deal, perhaps even to make a better offer for the other party to consider—no attacks on each other, no tearing down of other people's opinions, and no finger-pointing. Focus on moving forward and delivering optimal performance. And while on the quest to solve a problem, remember to involve all staff members that have an idea, including those who may have made poor decisions that led to the problem. Sure, not everyone's idea will be brilliant, but typically, ideas given in this context are not all wrong either. Put every idea on the table, pick the elements that make sense, and merge them. By the time you implement this, the issue will disappear. Conclusion As stated earlier, handling employees in any company can be a terrifying scenario, or it could be an enjoyable experience. It all depends on the strategies you apply to get them all moving happily towards the company's ultimate goal. These three tips work not only in a meeting set up but also for individual and collective relationships between employees and the company leadership. Now go out and have a productive meeting. About Author Frank Hamiltonhas been working asan editor atreview serviceOnline Writers Rating.He isa professional writing expert in such topics as blogging, digitalmarketingand self-education.He also loves traveling and speaks Spanish, French, German and English.

The key to any successful business is a hard-working team. Without a dedicated staff pushing the buttons behind the scenes, businesses would grind to a halt. However, keeping teams motivated can be a struggle, especially in the midst of high-pressure environments that are filled with deadlines, problem-solving, and stress. As motivation drops, so does productivity, so how can you ensure your staff is working at full capacity? Here are four tips on how to improve the productivity of your team. 1. Take advantage of efficiencies ‘Busy work’ is the bane of many employees’ lives. This kind of work is defined as tasks that have hours spent on them when a quicker alternative is available which could save a lot of time and energy. Take your Point of Sale proposition, for example, identifying what products are selling well and which need a push is one of the most key pieces of data when deciding to restock or not. Cutting out manual stock checks and implementing a modern POS system not only saves time but will ensure that human error never leaves you sold out of a bestselling product accidentally. Less busy work, fewer headaches, and more efficiency. 2. Improve workplace conditions Your place of work can be a major contributor to productivity if it allows your employees to feel comfortable whilst they’re working. Temperature can be a major factor in this, and maintaining a pleasant working temperature, while tricky, will ensure employees keep their minds focused on the job at hand rather than wondering why they didn’t pack an extra sweater. Creating ‘chill out’ zones for employees to enjoy on their breaks - or just to get away from the screen for a few minutes - are also invaluable. While it might seem counter-intuitive to create an area where employees are encouraged to down tools and take a period of relaxation, it’s been proven that these periods of respite more than make up for it in terms of the productivity levels they provide. 3. Provide opportunities for growth Delegation is a tricky topic for many managers, especially if they’re one of the founders of a business. Letting go and giving employees increased responsibility will not only help spread the workload but will also give employees much needed growth opportunities. While there is an element of risk involved in the delegation, these can be negated to ensure tasks are given to well respected and proven employees. Handing out these opportunities will not only save you time they will also give employees a chance to prove themselves and help them to step out of their comfort zones and grow professionally. 4. Keep your employees happy Probably the most important point of all. A happy workforce is a productive workforce and keeping spirits and morale high is proven to have a positive impact on performance. Happiness levels are also likely to lead to fewer sick days, fewer internal disputes, and create a less stressful atmosphere. Tackling the above issues will help employees feel valued, looked after, and challenged which are three crucial components of workforce happiness.

It’s hard to believe, but we’re now going on nearly six months of some type of self-quarantined work from home (along with our spouses, kids, roommates, and even pets). It may have taken some time to adapt, but for the most part, we succeeded in working remotely. Yet now the tides are changing again, both as schools and businesses all consider various reopening strategies. This situation may lead to more questions than answers. For example, does your company have a plan to accommodate the “new normal?” If so have you shared it with the workforce, or similarly, held discussions with employees to let them know what to expect? If you don’t know the answers to these questions, here are five strategies to help transition back into a traditional workplace as well as ways you can fully support your staff. 1. Prepare employees for your reopening strategy While reopening may not be as abrupt as the initial transition to remote work was, chances are good that things will still move quickly as more state governors, school districts, and local officials give their approval. Your company will have to decide how to move forward. If you don’t have a reopening strategy, take the time to formulate one now. Here are some questions to ask yourself: Do you plan to continue a remote-work model? Will you need to use a hybrid model, with some tasks being done onsite and others remotely? Is your workplace equipped to bring everyone back with safe distancing modifications? Once you have a strategy in place that seems feasible, let your employees know what the plan is, so they can assess their individual situations, such as child care requirements, transportation, or other details. Whatever workplace model you choose, you’ll need to simultaneously protect your business and support your employees. 2. Determine any staffing cuts and communicate them The global economic fallout from the COVID-19 pandemic is an ugly story yet to be fully told. While experts are making some dire projections, no one is really sure how it will develop, but rough roads may lie ahead for businesses and individuals alike. If the company is going to have to cut personnel or salaries to stay afloat, it’s important to communicate this to your employees so they can plan appropriately. Many will want to launch job searches, cut home expenses, examine their credit scores to see if they have access to borrowing funds with favorable terms, or make other financial arrangements. Give them the greatest opportunity possible to take care of themselves and their families. Additionally, if employees’ roles will be changing as the result of cuts or realignments, be sure you communicate this to your employees and give them the support they’ll need during any transitions. To learn even more about managing remote teams and keeping employees working at home highly engaged, download our ebook now. Download now 3. Plan for necessary infrastructure and policy changes You may need to revamp your physical workspace to meet your state’s or municipality’s requirements for reopening. This might include reconfiguration, physical restructuring, or installing floor markers or plexiglass barriers. Don’t wait until the last minute to start making these changes; the transition may take longer than you think to achieve compliance. Establish and communicate health-oriented policies (i.e., taking temperatures or cleaning procedures) to employees and customers alike. Outline the new responsibilities and expectations for both your company and its employees. Remember to order enough personal protective equipment for your employees’ use: include a stock of masks, disinfectant wipes, spray disinfectants, hand sanitizer, and soap. Be sure employees are equipped to enforce any directives that customers will have to follow to keep your business compliant, such as waiting outside your facility to limit customer numbers inside, maintaining social distancing and mask-wearing while inside, etc. For everyone to remain safe and healthy, it’ll be a team effort. 4. Offer employees tips and perks Many workers who had to go on unemployment have been using up their savings during the pandemic and could use financial assistance or at least resources and information to help them get by. Recognize employees for all their hard work and what they’ve endured while working at home. Reward them with points that they can redeem for gift cards, corporate items, or other “thank you” gifts. Evaluate life insurance plans and see if you can identify a discounted group plan to help employees feel more secure about their families’ futures. Many schools may start with distance learning in the fall, which means many employees will need help with child care. Explore on-site options, offer financial assistance for child care, or allow flexible work schedules to support your employees. After spending 24/7 at home for three months, many of your employees’ homes likely experienced a lot more wear and tear than they would have under normal circumstances. Suggest a good home warranty program to make sure they’re covered for problems not included in their homeowners’ insurance. Offer training or education opportunities to help employees feel more confident about being able to maintain their marketability in an economic downturn. Offering your employees a variety of perks or helpful ideas will go a long way toward boosting morale and confidence in a time where many feel frustrated by the ripple effects of the coronavirus pandemic. Michael HawkinsFranchise Owner of Interim HealthCare SLC “Interim Healthcare SLC needed HR technology, and we’re pleased with the results we’ve gained from LIKE.TG’s solutions for recruiting, onboarding, and employee engagement. Yet it’s an opportunity for all Interim franchises. It would be so great if each franchise owner could implement similar solutions to replace legacy systems that might not work as well as they should.” 5. Remember your customers Your customers are your company’s lifeblood, and you’ll want to keep this foremost in your mind (as well as your employees’ minds) as you formulate your reopening plans. Remember that many people are struggling financially, so it won’t pay to be too aggressive in your sales approach. Some customers will be actively looking to purchase, while others might regard your offerings more as “wish list” items at this time. But no matter what stage you’re approaching in the sales funnel, it’s always important to keep up consistent communication with your community. Send thoughtful emails, handwritten notes, or useful promotional gifts to maintain strong relationships with clients (these will prove to be well worth the investment). If you’re good to your customers — even when they’re not buying — they’ll return the favor when they’re financially able. Moving into a post-COVID-19 era will almost certainly be unlike anything we’ve experienced before. The better prepared you are to transition to the “new normal,” the more successful you’ll be when it happens. Additional Resources: Protect you business with Cerity - Workers' Comp InsuranceLearn how to start your own start-up Christopher BaggottChief Executive Officerator of Medlinks Cost Containment, Inc. and Medlinks Staffing, LLC. “Our staff has praised the increased communications level Workmates delivers. We use it to communicate important project matters and give staff specific ‘kudos’ or even recognize their birthdays. More importantly, we use Workmates to clarify important project details that needed rapid dissemination among the entire team.” Workmates For even more information on employee communication, collaboration, and engagement, we invite you to learn more about our Workmates employee experience platform. Sign up for a free, no-obligation demo to see how Workmates is changing employee management, in the new normal and beyond. About Author: This article is written by a marketing team member at LIKE.TG. LIKE.TG is a leading provider of proven HR solutions, including recruiting, onboarding, employee communications engagement, and rewards recognition. Our user-friendly softwareincreases employee productivity, delivers time and cost savings, and minimizes compliance risk.

As we head into fall and the uncertainty of the ongoing COVID-19 pandemic, it’s likely that your team is still working remotely. Employees may have even moved into different states to live with family members, or they may simply be working from the comfort of their own living room. Whatever the case may be, it can be difficult to get all employees on the same page when you’re suddenly faced with the challenges of managing a widely distributed workforce. Engaging all employees and making sure they’re highly motivated and as productive as possible is vital to your overall success. Distance should never be something that stops your company from achieving your most critical goals, pandemic or not. To help you stay on track, here are five, simple, straightforward, yet actionable ways you can boost morale (and productivity) in any distributed team. #1: Communicate! When you’re in an office and have a quick question, it is easy to lean over and quickly ask a neighbor or other team member for their insight. At least it used to be. Yet now, working from home makes that type of quick information exchange difficult, if not impossible. Most of the time, these types of questions don’t even require a formal meeting, email exchange, or even a phone call. At first glance, this may make it seem that, without the in-person connections, you could lose the valuable back-and-forth that employees need to remain productive. This doesn’t have to be the case at all, especially with the right employee experience (EX) software. For example, modern EX platforms now include powerful messaging apps that make natural communication fast, easy, and effective. These apps make it much easier to communicate quickly and consistently, and any employee can quickly create a group chat, or even a specific channel dedicated to a certain topic. Shirley GarciaAuditing and Operations Administrator of Medlinks Cost Containment, Inc. and Medlinks Staffing, LLC. “I can’t say this enough: Without LIKE.TG, we could not have communicated important project information or demonstrated that we could take on an increased workload. We now provide real-time workforce statistics and productivity reports that have helped us win more projects.” The benefit of these apps over email is clear: Email can be clunky and may lose the valuable, real-time insights if one employee is offline or just doesn’t answer in time. Additionally, email threads with multiple participants can quickly get off track or make it hard for employees to find the answers to a specific question. A simple chat (either one on one or with a group) eliminates all of these issues and provides the ideal way to share important information. #2. Schedule weekly team meetings Weekly team meetings are another indispensable part of working with a distributed team. In the past, in office-based environments, you probably held regular in-person meetings that kept everyone up-to-speed on the last updates related to projects, company details, or even employees’ personal development. Yet now, remote team members may lose this connection and could begin to feel isolated. It may be hard for them to hear company announcements, learn how they’re performing, or how they may be able to help with a specific project or task. Holding regularly scheduled team meetings, ideally as often once once a week, lets your employees know what’s happening, any COVID-19-related changes, and even how they’re doing. Tip: Not everyone has access to high speed internet or the right hardware, so be sure to take stock of everyone’s household amenities so you know whether anyone will have any roadblocks to completing work and attending meetings in the regular schedule. #3. Keep the team’s eye on the prize Another benefit of working in an office (and that can be difficult to replicate in remote-working models) is an overall sense of purpose. Whether employees are focused on hitting a quarterly revenue target, finishing a huge website relaunch, or just staying on top of their own goals, instilling a sense of community will help the entire team keep their eye on the prize. Knowing what they’re working toward, and how they are helping make a difference can be inspiring—and could give them the extra motivation they need while boosting team morale. Be sure to send out weekly or biweekly updates on the status of the larger project, so your team knows exactly where they stand and how they can keep improving the bigger picture. Here are a few more ways you can give your team a bird's-eye view: Develop or subscribe to a workflow dashboard that tracks your team’s progress in real time, showing when specific tasks are completed and how that affects the whole project. During your weekly meetings, be sure to bring up the state of recent projects, and let team members know what they can do to help keep workflow efficient and optimal. Make a work-wide calendar that shows general due dates and deadlines that must be met in order for your project to roll out on time. This can help visual learners stay on top of their personal priorities, #4. Reward hard workers Once you’ve got a project rolling and your team onboard, it’s time to assess everyone’s personal progress. Back at the office, you may have had an employee of the month award, or you may have given bonuses to workers who completed their work above and beyond expectations. It may not feel as important when you’re working remotely, but you should definitely keep these traditions going — or start them, if you didn’t have them before. Team members want to know that they will be rewarded for successfully taking on projects and showing leadership qualities. This will give them the morale they need to increase quality and output, too; it’s basically a win-win. If it’s in the budget, offer prizes or bonuses to employees who really stand out. #5: Don’t forget the fun stuff Finally, don’t forget to have fun with your team. Work isn’t always just about work — sometimes, letting off a little steam through a bonding event can be exactly what your team needs to feel more confident in themselves and in the company as a whole. Bonding while remote can seem like a challenge, but there are great options: Video chat bar trivia or poll to unleash everyone’s inner nerd Online multiplayer party games (paired with a video chat) The simple, classic video chat happy hour A watch party where everyone can tune in to see a streaming movie Really, the options are endless once you get creative. Empower remote teams Don’t let morale sink just because everyone is working from home. With the right strategies, innovative techniques, and fun bonding events, you can boost your team’s spirits and get everyone in the right mindset to complete their work happily and efficiently. The perfect platform for managing a remote workforce For even more information on employee communication, collaboration, and engagement, we invite you to learn more about our Workmates employee experience platform. Sign up for a free, no-obligation demo to see how Workmates is changing employee management, in the new normal and beyond. To learn more about giving access to vital HR and work information — even when they're not in the office. Download our ebook now. Download now About Author: Samantha Rupp holds a Bachelor of Science in Business Administration and is the managing editor for 365businesstips.com. She lives in San Diego, California and enjoys spending time on the beach, reading up on current industry trends, and traveling.