Visual authoring in Azure Data Factory

The Azure Data Factory user interface experience (UX) lets you visually author and deploy resources for your data factory without having to write any code. You can drag activities to a pipeline canvas, perform test runs, debug iteratively, and deploy and monitor your pipeline runs. There are two approaches for using the UX to perform visual authoring:

  • Author directly with the Data Factory service.
  • Author with Visual Studio Team Services (VSTS) Git integration for collaboration, source control, or versioning.

Author directly with the Data Factory service

Visual authoring with the Data Factory service differs from visual authoring with VSTS in two ways:

  • The Data Factory service doesn't include a repository for storing the JSON entities for your changes.
  • The Data Factory service isn't optimized for collaboration or version control.

Configure the Data Factory service

When you use the UX Authoring canvas to author directly with the Data Factory service, only the Publish All mode is available. Any changes that you make are published directly to the Data Factory service.

Publish mode

Author with VSTS Git integration

Visual authoring with VSTS Git integration supports source control and collaboration for work on your data factory pipelines. You can associate a data factory with a VSTS Git account repository for source control, collaboration, versioning, and so on. A single VSTS Git account can have multiple repositories, but a VSTS Git repository can be associated with only one data factory. If you don't have a VSTS account or repository, follow these instructions to create your resources.


You can store script and data files in a VSTS GIT repository. However, you have to upload the files manually to Azure Storage. A Data Factory pipeline does not automatically upload script or data files stored in a VSTS GIT repository to Azure Storage.

Configure a VSTS Git repository with Azure Data Factory

You can configure a VSTS GIT repository with a data factory through two methods.

Configuration method 1: Let's get started page

In Azure Data Factory, go to the Let's get started page. Select Configure Code Repository:

Configure a VSTS code repository

The Repository Settings configuration pane appears:

Configure the code repository settings

The pane shows the following VSTS code repository settings:

Setting Description Value
Repository Type The type of the VSTS code repository.
Note: GitHub is not currently supported.
Visual Studio Team Services Git
Azure Active Directory Your Azure AD tenant name.
Visual Studio Team Services Account Your VSTS account name. You can locate your VSTS account name at https://{account name} You can sign in to your VSTS account to access your Visual Studio profile and see your repositories and projects.
ProjectName Your VSTS project name. You can locate your VSTS project name at https://{account name}{project name}.
RepositoryName Your VSTS code repository name. VSTS projects contain Git repositories to manage your source code as your project grows. You can create a new repository or use an existing repository that's already in your project.
Collaboration branch Your VSTS collaboration branch that will be used for publishing. By default, it is master. Change this in case you want to publish resources from another branch.
Root folder Your root folder in your VSTS collaboration branch.
Import existing Data Factory resources to repository Specifies whether to import existing data factory resources from the UX Authoring canvas into a VSTS Git repository. Select the box to import your data factory resources into the associated Git repository in JSON format. This action exports each resource individually (that is, the linked services and datasets are exported into separate JSONs). When this box isn't selected, the existing resources aren't imported. Selected (default)

Configuration method 2: UX authoring canvas

In the Azure Data Factory UX Authoring canvas, locate your data factory. Select the Data Factory drop-down menu, and then select Configure Code Repository.

A configuration pane appears. For details about the configuration settings, see the descriptions in Configuration method 1.

Configure the code repository settings for UX authoring

Switch to a different Git repo

To switch to a different Git repo, locate the icon in the upper right corner of the Data Factory overview page, as shown in the following screenshot. If you can’t see the icon, clear your local browser cache. Select the icon to remove the association with the current repo.

After you remove the association with the current repo, you can configure your Git settings to use a different repo. Then you can import existing Data Factory resources to the new repo.

Remove the association with the current Git repo.

Use version control

Version control systems (also known as source control) let developers collaborate on code and track changes that are made to the code base. Source control is an essential tool for multi-developer projects.

Each VSTS Git repository that's associated with a data factory has a a collaboration branch. (master is the default collaboration branch). Users can also create feature branches by clicking + New Branch and do development in the feature branches.

Change the code by syncing or publishing

When you are ready with the feature development in your feature branch, you can click Create pull request. This will take you to VSTS GIT where you can raise pull requests, do code reviews, and merge changes to your collaboration branch. (master is the default). You are only allowed to publish to the Data Factory service from your collaboration branch.

Create a new pull request

Publish code changes

AFter you have merged changes to the collaboration branch (master is the default), select Publish to manually publish your code changes in the master branch to the Data Factory service.

Publish changes to the Data Factory service


The master branch is not representative of what's deployed in the Data Factory service. The master branch must be published manually to the Data Factory service.

Use the expression language

You can specify expressions for property values by using the expression language that's supported by Azure Data Factory.

Specify expressions for property values by selecting Add Dynamic Content:

Use the expression language

Use functions and parameters

You can use functions or specify parameters for pipelines and datasets in the Data Factory expression builder:

For information about the supported expressions, see Expressions and functions in Azure Data Factory.

Add Dynamic Content

Provide feedback

Select Feedback to comment about features or to notify Microsoft about issues with the tool:


Next steps

To learn more about monitoring and managing pipelines, see Monitor and manage pipelines programmatically.