Which component allows Azure Data Factory to track changes more easily by integrating with source control?

The integration of Azure Data Factory with source control is primarily facilitated through Git configuration. This feature allows users to manage their Data Factory resources more effectively by enabling version control, which is essential for collaborative development and maintaining a history of changes made to data pipelines, datasets, and linked services.

By configuring Git within Azure Data Factory, teams can leverage version control systems like GitHub or Azure DevOps Git. This integration helps in tracking changes, handling pull requests, and even rolling back to previous versions if necessary. It ensures that multiple team members can work on the same project without conflicts, as changes can be merged properly, and the history of all modifications is preserved.

In contrast, the other components mentioned—Data Flow, Activity run, and Pipeline management—do not inherently provide the functionality for integrating with source control. Data Flow refers to a visual representation of data transformations, Activity runs pertain to operations executed within pipelines, and Pipeline management focuses on the orchestration of data workflows. While all these components play vital roles in data processing and workflows, they do not directly facilitate tracking changes or integrating with source control systems.