In this article, we’ll be going over two methods you can use to load data from Amazon RDS to Azure Synapse Analytics: using Aurora Read Replica and a third-party, no-code data replication tool.

Note: We’ll be migrating data from SQL Server on Amazon RDS in the article. Amazon RDS for SQL Server simplifies the process of operating, configuring, and scaling SQL Server on the cloud.

Methods to Replicate Data from Amazon RDS to Azure Synapse

Using Azure Data Factory for Amazon RDS Azure Synapse Migration

This Amazon RDS for SQL Server connector can be used for:

  • Copying data through Windows or SQL validation.
  • SQL Server version 2005 and above.
  • Extracting data by using a stored procedure or an SQL query.

Based on the location of your data store, you need to keep the following prerequisites in mind:

  • You can use the Azure integration Runtime if your data store is a managed cloud data service. If the access is limited to IPs approved by firewall rules, you’ll have to grant access to the Azure Integration Runtime IPs.
  • You need to set up a self-hosted integration runtime to connect to your data store if it is located in an on-premises network, an Amazon Virtual Private Cloud, or an Azure virtual network.

For this Amazon RDS to Azure Synapse ETL method, you’ll first need to create an Amazon RDS for SQL Server-linked service in the Azure portal UI.

  1. Go to the Manage tab in your Azure Synapse workspace, choose Linked Services, and click on the +New button. 
Amazon RDS to Azure Synapse: Create Linked Service
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  1. Look for the Amazon RDS for SQL Server connector and select it.
Amazon RDS to Azure Synapse: Selecting the Connector
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  1. Fill in the details, test the connection, and create the new linked service.
Amazon RDS to Azure Synapse: Configuring Details for the Linked Service
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  1. Repeat the steps for Azure Synapse Analytics and enter the corresponding connection credentials. For Azure Synapse Analytics, managed identity, SQL authentication, and service principal are currently supported.
  2. Next, you’ll need to create a pipeline that contains a copy activity that will transfer data from Amazon RDS for SQL Server into a dedicated SQL pool.
  3. Navigate to the Integrate tab, select the plus icon next to the pipelines header, and click on Pipeline
  4. Under Move and Transform in the activities pane, drag Copy data to the pipeline canvas.
  5. After selecting the copy activity, go to the Source tab and click on the New button to create a new source dataset.
    • Select Amazon RDS for SQL Server as your data store and pick DelimitedText as your format.
    • Within the set properties pane, choose Amazon RDS for SQL Server linked service you created. Mention the file path of your source data and mention whether the first row has a header. You can get the schema from a file or a sample. Click on OK when you’re done.  
  6. Scroll to the Sink tab. Press the New button to make a new sink dataset. Choose Azure Synapse Analytics as your data store and click on Continue.
    • Choose the Azure Synapse Analytics linked service you created in the set properties pane. If you’re writing to an existing table, pick it from the dropdown. Or, you can check the Edit option and enter a new table name. Click on OK when you’re done with your customizations. 
    • If you’re creating a table, you can enable the Auto create table option in the table option field.

After you’ve finished setting up your pipeline, you can perform a debug run before you publish your pipeline to make sure everything is correct.

  1. To debug the pipeline, choose Debug from the toolbar. You can view the status of the pipeline run in the Output tab at the bottom of the window.
  2. Once your pipeline has run successfully, you can select the Publish All option. This’ll publish all the components (pipelines and datasets) you created to the Synapse Analytics service.
  3. Wait for the Successfully Published message, and you’re good to go.

 If you want to manually trigger the pipeline published in the previous step:

  1. Choose Add Trigger from the toolbar, and then select Trigger Now. On the Pipeline Run page, click on the Finish button.
  2. Scroll to the Monitor tab in the left sidebar to view pipeline runs triggered by manual triggers. You can use links in the Actions column to rerun the pipelines and look at activity details.
  3. If you want to view the activity runs related to the pipeline run, choose the View Activity Runs link in the Actions column. For this example, since you only have one activity, you’ll only see one entry in the list. For copy operation details, you can select the Details link from the Actions column. Choose Pipeline Runs at the top to return to the Pipeline Runs view. To refresh the view, click on the Refresh option. 
  4. Verify if your data is correctly written in the dedicated SQL pool.

So, Azure Data Factory sounds like the perfect tool to replicate data from any source to Azure Synapse given that they belong to the same suite of products. But, here are a few scenarios where Azure Data Factory might not be the ideal candidate for your replication needs:

  • Azure-based Integrations: Azure Data Factory wouldn’t be enough if you’re planning to replicate data from sources outside the Azure environment. Therefore, it works fine for Azure-based data sources, but for other sources, you might need to look for an alternative.
  • Analysis Ready Raw Data: The Copy Data tool doesn’t have a lot of transformation capabilities. Therefore, if you’re planning on using it to replicate data, you’ll need to ensure the data is clean, and analysis-ready beforehand. 
  • One-Time Migration: Azure’s consumption-based pricing might seem like a great idea on paper, but a long-term data pipeline might be a little heavy on your pocket. So, for long-term and repetitive data replication you might want to invest in an alternative.

If you’re facing any or all of these challenges, you can use a no-code data replication solution that completely manages and maintains the data pipelines for you.

Using a No-Code Data Replication Tool to Replicate Data from Amazon RDS to Azure Synapse

You can streamline the Amazon RDS Azure Synapse integration process by opting for an automated tool to:

  • Focus on pressing engineering goals and free up the resources needed for them.
  • Save time spent on data preparation thanks to a user-friendly UI.
  • Enable business teams to quickly create accurate reports with no-code data replication tools.
  • Access to near real-time data without sacrificing accuracy or consistency.
  • Consolidate analytics-ready data for performance measurement and opportunity exploration.

Let’s take a look at the simplicity a cloud-based ELT tool like Hevo provides to your workflow:

Step 1: Configure Amazon RDS SQL Server as a Source

Amazon RDS to Azure Synapse: Configure Source
Amazon RDS to Azure Synapse: Configure Source

Step 2: Configure Azure Synapse Analytics as a Destination

Amazon RDS to Azure Synapse: Configure Destination
Amazon RDS to Azure Synapse: Configure Destination

And that’s it! Based on your inputs, Hevo will start replicating data from Amazon RDS to Azure Synapse Analytics.

Point to note: Hevo doesn’t support replication of data to serverless SQL pools in Azure Synapse Analytics.

Suppose you’d like to dive deeper into how your Amazon RDS Azure Synapse pipelines are configured for this use case. In that case, you can read the official documentation for configuring Amazon RDS SQL Server as a source and Azure Synapse Analytics as a destination

What can you achieve by replicating data from Amazon RDS to Azure Synapse?

By migrating your data from Amazon RDS to Azure Synapse, you will be able to help your business stakeholders with the following:

  • Aggregate the data of individual interaction of the product for any event. 
  • Finding the customer journey within the product (website/application).
  • Integrating transactional data from different functional groups (Sales, marketing, product, Human Resources) and finding answers. For example:
    • Which Development features were responsible for an App Outage in a given duration?
    • Which product categories on your website were most profitable?
    • How does the Failure Rate in individual assembly units affect Inventory Turnover?


In this article, we’ve talked about two ways that you can use to replicate data from Amazon RDS to Azure Synapse: via Azure Data Factory and through a no-code data replication tool, Hevo.

Hevo allows you to replicate data in near real-time from 150+ sources like Amazon RDS SQL Server to the destination of your choice including Azure Synapse Analytics, BigQuery, Snowflake, Redshift, Databricks, and Firebolt, without writing a single line of code. We’d suggest you use this data replication tool for real-time demands that require you to pull data from SaaS sources. This’ll free up your engineering bandwidth, allowing you to focus on more productive tasks.

For rare times things go wrong, Hevo ensures zero data loss. To find the root cause of an issue, Hevo also lets you monitor your workflow so that you can address the issue before it derails the entire workflow. Add 24*7 customer support to the list, and you get a reliable tool that puts you at the wheel with greater visibility.

If you don’t want SaaS tools with unclear pricing that burn a hole in your pocket, opt for a tool that offers a simple, transparent pricing model. Hevo has 3 usage-based pricing plans starting with a free tier, where you can ingest up to 1 million records.

Schedule a demo to see if Hevo would be a good fit for you, today!


Content Marketing Manager, Hevo Data

Amit is a Content Marketing Manager at Hevo Data. He is passionate about writing for SaaS products and modern data platforms. His portfolio of more than 200 articles shows his extraordinary talent for crafting engaging content that clearly conveys the advantages and complexity of cutting-edge data technologies. Amit’s extensive knowledge of the SaaS market and modern data solutions enables him to write insightful and informative pieces that engage and educate audiences, making him a thought leader in the sector.

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