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To build successful data analytics projects with Copy job, it is very important to have source control, continuous integration, continuous deployment, and collaborative development environments.
In Fabric, there are two features we currently support in collaboration with the Application Lifecycle Management (ALM) team: Git integration and deployment pipelines. These features allow users to import/export workspace resources with individual updates.
With Git integration and deployment pipeline supported for Copy job, users can leverage their own Git repositories in Azure DevOps or GitHub and utilize Fabric’s built-in Deployment Pipelines, enabling seamless CI/CD workflows. This integration marks an important step toward expanding CI/CD capabilities across all Fabric items, empowering users with advanced, reliable development tools for their data projects.
Take the following steps to set up Git integration for your Copy job in Data Factory:
To access Git with your Microsoft Fabric workspace, ensure the following prerequisites for both Fabric and Git.
To use Git integration with Copy job in Fabric, you first need to connect to a Git repository, as described here.
Sign into Fabric and navigate to the workspace you want to connect to Git.
Select Workspace settings.
Select Git integration.
Select your Git provider. Currently, Fabric only supports Azure DevOps or GitHub. If you use GitHub, you need to select Add account to connect your GitHub account. After you sign in, select Connect to allow Fabric to access your GitHub account.
Once you connect to a Git repository, you need to connect to a workspace, as described here.
From the dropdown menu, specify the following details about the branch you want to connect to:
For Azure DevOps branch connections, specify the following details:
For GitHub branch connections, specify the following details:
Select Connect and sync.
After you connect, the Workspace displays information about source control that allows users to view the connected branch, the status of each item in the branch, and the time of the last sync.
You can now commit changes to Git, as described here.
After the changes are committed, the items that were committed are removed from the list, and the workspace will point to the new commit that it synced to.
Take the following steps to use Git deployment pipelines with your Fabric workspace.
Before you get started, be sure to set up the following prerequisites:
After creating a pipeline, you need to add content you want to manage to the pipeline. Adding content to the pipeline is done by assigning a workspace to the pipeline stage. You can assign a workspace to any stage. Follow the instructions to Assign a workspace to a pipeline.
When you finish working with content in one pipeline stage, you can deploy it to the next stage. Deployment pipelines offer three options for deploying your content:
After you choose how to deploy your content, you can review your deployment and leave a note.
Once you have content in a pipeline stage, you can deploy it to the next stage, even if the next stage workspace has content. Paired items are overwritten. You can learn more about this process, in the Deploy content to an existing workspace section.
You can review the deployment history to see the last time content was deployed to each stage. To examine the differences between the two pipelines before you deploy, see Compare content in different deployment stages.
The following known limitations apply to CI/CD for Copy job in Data Factory in Microsoft Fabric:
Events
31 Mar, 23 - 2 Apr, 23
The biggest Fabric, Power BI, and SQL learning event. March 31 – April 2. Use code FABINSIDER to save $400.
Register todayTraining
Module
Implement continuous integration and continuous delivery (CI/CD) in Microsoft Fabric - Training
Learn the key concepts and strategies for implementing continuous integration and continuous deployment (CI/CD) in Microsoft Fabric.
Certification
Microsoft Certified: Fabric Data Engineer Associate - Certifications
As a Fabric Data Engineer, you should have subject matter expertise with data loading patterns, data architectures, and orchestration processes.