How to control version control of azure data factory?

Version control 1 Creating feature branches. Each Azure Repos Git repository that's associated with a data factory has a collaboration branch. ... 2 Configure publishing settings. By default, data factory generates the Resource Manager templates of the published factory and saves them into a branch called adf_publish. 3 Publish code changes. ...
In addition, why does Azure Data Factory not show version control?
Another issue that can occur is when the code repository seem to be configured, but the data factory cannot show signs of version control being in place. The reason for this is often related to how the Azure subscription is setup and what role you have been assigned.
And, how to assign roles to data factories in azure? If you want to allow access to any data factory in a subscription, assign the role at the subscription level. Let a user view (read) and monitor a data factory, but not edit or change it. Assign the built-in reader role on the data factory resource for the user. Let a user edit a single data factory in the Azure portal.
In respect to this, can You Save changes to repository in Azure Data Factory?
When working directly under the Azure Data Factory mode, which is the default mode, you will have no option to save the changes in a repository before publishing it to the production environment, where the only option is to publish the changes directly to the Data Factory service, which contains no versioning control or tracking process.
Keeping this in consideration, do you need a resource manager for Azure Data Factory?
The Azure Resource Manager template required to deploy Data Factory itself is not included. To provide a better authoring experience, Azure Data Factory allows you to configure a Git repository with either Azure Repos or GitHub. Git is a version control system that allows for easier change tracking and collaboration.

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