Securing Your Data Pipelines: Best Practices for Fabric Data Factory

In today’s data-driven world, securing your data pipeline is critical to protecting sensitive information and complying with regulatory requirements. Microsoft Fabric Data Factory Experience (FDF) provides a robust set of security features to protect data through the various stages of the data workflow. In this post, we will look at the main security features of FDF and show how to implement them through real-world examples.

Key security features of Fabric Data Factory

Before we start the implementation, let’s take a look at the basic security mechanisms that Fabric Data Factory provides.

1. proof: Fabric Data Factory uses Microsoft Entra IDs to authenticate users (or service principals). Once authenticated, the user receives: access token From Microsoft Entra ID. Fabric uses these tokens to perform operations in the user context.
2. authorization: All Fabric permissions are stored centrally by the metadata platform. The Fabric Service retrieves authentication information and queries the metadata platform as needed to authenticate and validate user requests.
   
3. data encryption:
   Save data: All Fabric data stores are encrypted at rest using Microsoft-managed keys. Fabric data includes customer data as well as system data and metadata.
   Data in transit: Data transmitted between Microsoft services is always encrypted with TLS 1.2 or higher. Fabric negotiates TLS 1.3 whenever possible. Traffic between Microsoft services is always routed through: Microsoft Global Network.
4. Management ID: A A Fabric workspace identity is an automatically managed service principal that can be associated with a Fabric workspace. A fabric workspace with a workspace ID can securely read from or write to a firewall-backed Azure Data Lake Storage Gen2 account via: Trusted workspace access For shortcut to OneLake. Fabric items can use identities when connecting to resources that support Microsoft Entra authentication. Fabric uses the workspace ID to obtain Microsoft Entra tokens without requiring customers to manage credentials.
5. Key Vault Integration: Unfortunately, starting today, Key Vault integration is Not supported Data pipeline connectivity in Dataflow Gen 2/Fabric.
6. network security: When you connect to a pipeline over a private link, you can use the data pipeline to load data from any data source with a public endpoint into a private link-enabled Microsoft Fabric Lakehouse. Customers can also use a private link to build and operationalize data pipelines with activities including notebooks and data flow activities. However, you cannot currently copy data from or to the data warehouse if Private Link in Fabric is enabled.

Now let’s look at an example that shows how to secure a data pipeline in Fabric Data Factory (FDF).

Example: Securing a Data Pipeline in Fabric Data Factory

script:

We are setting up a data pipeline to move sensitive data from ADLS Gen 2 to our Fabric warehouse. To ensure the security of this pipeline:

Prerequisites:
– Tools and skills needed:

• Azure Data Lake Gen2 (ADLS) storage account.
• Knowledge of Azure Data Factory.
• Fabric workspace.

step:

Step 1: Enable managed identity at the workspace level for your Fabric Data Factory pipeline

The workspace identity is as follows: Created and deleted by workspace administrator.. The workspace ID has the Workspace Contributor role for the workspace.

Workspace ID is supported for authentication to the target resource of the connection. Only users with the Administrator, Member, or Contributor role on a workspace can configure a workspace ID for authentication when connecting.

Managed identities allow Fabric Data Factory to securely authenticate to other Azure services without hardcoding credentials.

1. Go to your workspace and open Workspace Settings.
2. select Workspace identity tag.
3. select + Workspace identity button.
   

Once activated, you can use this identity to securely access resources such as Azure SQL Database.

Step 2: Configure trusted workspace access in ADLS Gen2

• Sign in to the Azure portal and go to: Custom Deployment.
• choose Create your own template in the editor. For a sample ARM template to create resource instance rules, see: ARM template sample.
  

• Create a resource instance rule in the editor. When finished, select Next. Review + Create.
• to basic In the tabs that appear, specify the required project and instance details. When finished, select Next. Review + Create.
• to Review + Create In the tab that appears, review the summary and then make. The rule is submitted for deployment.

Once the deployment is complete, you can navigate to your resources.

Step 3: Create a Pipeline to Connect to ADLS gen2 and Copy Data to Fabric Lakehouse

This pipeline connects directly to a firewall-backed ADLS Gen2 account with Trusted Workspace Access enabled.

• Go to your workspace and click New Item.
• Create a new pipeline
  

• In your pipeline, add a Copy to Canvas activity.
  
• On the Copy Activity Source tab: Select ADLS Gen2 as the data source and connect.
  
• In the Destinations tab, connect to the Lakehouse and select a table.
  

Step 4: Results

Once the copy activity has finished running, you can view the data in Lakehouse.

conclusion

– Securing your data pipeline in Azure Data Factory is essential to maintaining the integrity, confidentiality, and availability of your data. By leveraging features like managed identities, you can build a strong security framework for your data flows.

Do you have any other tips for securing your Fabric Data Factory? Let us know in the comments!

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