Maintaining Data Pipelines with Version Control and Git

Maintaining data pipelines can be a daunting task, especially when multiple developers are working on the same pipeline. Version control is an essential tool for managing the pipeline’s code, configuration, and metadata. In this lesson, we’ll discuss how to maintain data pipelines with version control in Azure Data Factory and perform our version control activities using GitHub.

Version control in data pipelines

Version control, in the context of data pipelines, is a systematic approach to managing and tracking changes to the configuration, code, and definitions of data pipelines over time. It ensures that every modification to the pipeline is documented, allowing developers to view and revert back to previous versions if needed. By maintaining data pipelines through version control, teams can collaborate efficiently, easily track changes made by different members, and avoid conflicts during integration. This practice establishes a historical record of pipeline changes, facilitating effective troubleshooting and debugging when issues arise.

Commonly used version control tools for data pipelines include Git, Apache Subversion (SVN), and Mercurial. These tools provide features for versioning, branching, and merging, enabling smooth collaboration and managing complex codebases.

In the context of production software, version control plays a vital role in the Continuous Integration/Continuous Deployment (CI/CD) process. It helps automate the deployment of data pipelines to production, ensuring that only thoroughly tested and validated changes are promoted to the live environment. By maintaining a version control system, teams can confidently iterate and update their data pipelines while ...