Integrating MySQL on Amazon RDS to Azure Synapse can offer a seamless data pipeline, enabling you to leverage the strengths of both for enhanced data processing and analytics.

Amazon RDS offers a fully-managed and scalable relational database service, providing seamless deployment. On the other hand, Azure Synapse is a comprehensive analytics platform that allows you to handle abundant data and deliver insights at lightning speed.

By connecting AWS RDS for MySQL to Synapse, you can combine the benefits of a relational database and a high-performance analytics platform. This will help you achieve several advantages, including efficient data transformation and elevate your business’s data-driven strategies to make real time insights. For instance, consolidating data in Synapse will assist you in analyzing your customers’ journey and reveal buying patterns, allowing you to enhance customer experience.

Read on to discover the two straightforward methods explained in this article to achieve a seamless data pipeline. 

Method 1: Load Data from MySQL on Amazon RDS to Azure Synapse

This approach divides the integration and replication process into three distinct steps. To begin, we’ll export data from AWS RDS for MySQL into CSV files. Next, the downloaded files will be uploaded to Azure Blob Storage. Finally, the data will be replicated from the storage container to Synapse.

To build MySQL on Amazon RDS to Azure Synapse ETL pipeline, simply follow these steps:

Step 1: Move Data from AWS RDS for MySQL into CSV Files

  • Establish a connection with your AWS RDS MySQL instance
  • Log in to your AWS Management Console.
  • Navigate to the Amazon RDS service and select your MySQL database instance.
  • On the Connectivity & Security tab, get the connection details, including the endpoint, port, username, and password.
  • Access MySQL Database
  • Use a MySQL client tool or command-line utility like MySQL Workbench or MySQL CLI to connect to your RDS MySQL instance using the database details.
  • Export Data to CSV
  • Run a SQL query to export the data into a CSV file using the SELECT INTO OUTFILE statement. Here’s an example of a SQL query to export data from a table named “Amazon_rds” into a CSV file named information.csv:
INTO OUTFILE '/information.csv' 
FROM Amazon_rds;
  • After executing this, check the specified path to ensure that the CSV file has been created and contains the desired data.

Step 2: Load CSV to Azure Blob Storage

To load CSV files to Azure Synapse Analytics, you would need to use an intermediary storage service like Azure Blob Storage. It is a cloud-based object storage service provided by Microsoft Azure. Azure Blob Storage seamlessly integrates with other Azure services and tools, making it simple to build data-driven workflows within the Azure ecosystem.

To begin, copy the CSV files from the local machine to Azure Blob Storage using Azure CLI (AzCopy) or Azure portal. Then use Azure Blob Storage connector to transfer the data from Azure Blob Storage to Azure Synapse Analytics. 

Here we’ll proceed with the AzCopy command to replicate CSV files data to Azure Synapse.

  • Install and configure Azure CLI.
  • Open a terminal and run the following command to sign in to your Azure account:
az login
  • Create a new Azure Blob Storage Container using the following command:
az storage container create --Azure Blob Storage container --account name --account key

Replace ‘Azure Blob Storage container’ with desired container name, ‘account name’, and ‘account key’ with your Azure Blob Storage account name and key.

  • Copy CSV files to Azure Blob Storage Container using AzCopy:
azcopy copy '/path/to/local/directory/*.csv' 
--your-storage-account-name --your-storage-account-key

Replace ‘/path/to/local/directory/*.csv’ with the local directory path containing the CSV files. Mention the URL of the Azure Blob Storage container where you want to copy the CSV files.

The Azcopy command will upload all CSV files with .csv extension from the specified local directory to the Azure Blob Storage container.

Step 3: Upload to Azure Synapse

  • Create a new data flow in Azure Synapse Studio.
  • Navigate to the Develop hub and click on the + New button to create a new Data Flow.
  • Add Source Transformation
  • In the data flow canvas, drag and drop the Source transformation.
  • Use the Azure Blob Storage connector as the data source.
  • Configure the source transformation to point to your Azure Blob Storage container containing CSV files. Provide the necessary connection details, such as Azure Blob Storage account name, container name, and access key.
  • Add Sink Transformation
  • Drag and drop the Sink transformation. 
  • Choose Azure Synapse Analytics as the data sink and provide the required connection details, including server name, database name, and authentication method.
  • You can auto-detect the column mapping function or manually map them using the Mapping tab in the Sink transformation.
  • Preview and Execute
  • Click on the Data Preview tab to preview the data coming from the source transformation.
  • After validating the data click on Publish. Execute the data flow to move data from Azure Blob Storage to Azure Synapse.

You’ve successfully established MySQL on Amazon RDS Azure Synapse integration.

While using a manual method and CSV files is time-consuming and effort-intensive, it is better suited for one-time backups. The manual approach with CSV files offers a feasible solution for one-time transfers from RDS MySQL to Synapse with small data volumes. It is efficient and manageable for one-time backups, as you don’t need to invest in specialized software.

Limitations of Manual Migration from MySQL on Amazon RDS to Azure Synapse

When loading data from MySQL on Amazon RDS to Azure Synapse using CSV files, there are several limitations to keep in mind:

  • Latency: The data from AWS RDS MySQL needs to be exported in CSV format and saved in Azure Blob Storage before loading it into Azure Synapse. This introduces additional steps and can be time-consuming for larger datasets. It also requires regular manual interventions for data updates, data integrity, and schema mapping, which could impact data availability.
  • File Size Limitations: In Azure Blob Storage, a block refers to a smaller piece or chunk of data that creates a larger file called a Block Blob. A Block Blob can include up to 50,000 blocks (5TB), and each block can be up to 100 MB in size. If your CSV files are greater than 100 MB, you might need to split them into blocks to accommodate the block size limitation.

As this process involves manual intervention at each stage, here’s an alternative!

Method 2: Using a No-Code Tool for MySQL on Amazon RDS Azure Synapse Migration

Hevo is a cloud-based data replication service that allows you to effortlessly extract, transform, and load data across multiple sources and destinations. With its intuitive interface, Hevo enables both technical and non-technical users to set up data pipelines in minutes.

Hevo supports 150+ data sources (including 50+ free sources) and integrates seamlessly with popular cloud data warehouses and databases without the need for complex coding. Its real time data ingestion capabilities ensure data is continuously synced and available for analysis and reporting without any delays.

Here are the steps involved in MySQL on Amazon RDS Azure Synapse integration using the Hevo platform:

Step 1: Configure Amazon RDS MySQL as Source

MySQL on Amazon RDS to Azure Synapse: Configure Source
Image Source

Step 2: Configure Azure Synapse as Destination

MySQL on Amazon RDS to Azure Synapse: Configure Destination
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That’s it! You’ve successfully set up your Amazon RDS MySQL to Azure Synapse pipeline in two steps using the Hevo platform.

Hevo Data is a preferable choice for a no-code and automated data replication tool for several reasons, including

  • Real Time Data Streaming: Hevo supports real time data streaming, enabling you to extract and replicate data into the destination in near real time. This allows you to gain immediate insights and make data-driven decisions without delays.
  • Data Transformation: Hevo provides built-in drag-and-drop data transformation functions, allowing you to clean, filter, and enrich data before loading it into the destination. On Hevo, custom transformations can also be implemented using Python or SQL.
  • Monitoring: Hevo offers monitoring features to track data pipelines’ performance. You can also set alerts to get notified of any issues or failures during data transfers.

What Can You Achieve by Loading Data from MySQL on Amazon RDS to Azure Synapse?

  • Deeper Customer Insights: By combining data from Amazon RDS MySQL with other sources in Azure Synapse, you can create a centralized data repository. This allows you to correlate and analyze customer data from various channels, like website interactions, social media engagement, and purchase history. With this consolidated data in Synapse, you can immediately identify customer buying trends and patterns, leading to quick insights.
  • Understand your Team Better: A MySQL on Amazon RDS Azure Synapse integration helps you analyze team performance metrics. For instance, you can integrate with project management software and track project completion rates, task duration, and individual team member contribution. Understanding the team’s composition can help identify areas for improvement for better productivity.
  • Understand your Customers Better: Unifying data provides valuable insights into customer behavior and preferences, allowing you to map customer journeys. Understanding the customer journey helps identify pain points, improve customer experience, and optimize marketing and sales efforts.


Now you know two effective ways to seamlessly integrate MySQL on Amazon RDS to Azure Synapse. The first method, involving CSV files and Azure Blob Storage, provides a manual yet accessible approach for one-time transfer or smaller datasets. But the file size limitations and manual interventions can be drawbacks for large-scale transfers. On the other hand, the no-code Hevo Data platform streamlines the process, overcoming limitations with data transformation, schema validation, and real time streaming. It empowers you to bypass manual processes, ensuring data accuracy and reliability. Whether you prefer the flexibility of handling CSV files or the simplicity of Hevo’s no-code platform, both methods lead to enriched data analysis.

You can connect your SaaS platforms, databases, etc., to any data warehouse you choose, without writing any code or worrying about maintenance. If you are interested, you can try Hevo by signing up for the 14-day free trial.

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Tejaswini Kasture
Technical Content Writer, Hevo Data

Tejaswini is a passionate data science enthusiast and skilled writer dedicated to producing high-quality content on software architecture and data integration. Tejaswini's work reflects her deep understanding of complex data concepts, making them accessible to a wide audience. Her enthusiasm for data science drives her to explore innovative solutions and share valuable insights, helping professionals navigate the ever-evolving landscape of technology and data.

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