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 preserving stability and consistency in the production environment. This practice ensures that production data pipelines are reliable, auditable, and can be easily rolled back in case of unforeseen issues, ultimately contributing to the overall success and reliability of data-driven applications.

ADF pipeline version control

ADF offers seamless integration with GitHub, allowing users to leverage the power of version control using Git repositories. By connecting ADF to a GitHub repository, developers can efficiently manage code changes, create branches for different features or experiments, and merge changes back into the main pipeline codebase. This integration facilitates smooth collaboration, ensures code consistency, and simplifies the process of reviewing and validating changes before promoting them to production.

Moreover, version control in ADF significantly contributes to Continuous Integration/Continuous Deployment (CI/CD) practices. Developers can set up CI/CD pipelines that automatically build and deploy data pipelines when changes are pushed to the connected GitHub repository. This enables automated testing, validation, and deployment of data pipelines to different environments, including production. CI/CD integration ensures that only tested and validated changes are promoted to the live environment, reducing the risk of potential errors and improving the overall reliability of data-driven applications. By leveraging version control and CI/CD opportunities in Azure Data Factory, organizations can streamline data pipeline development, enhance collaboration, and confidently deliver high-quality data solutions at scale.

Create a GitHub repository

1. Go to GitHub and sign in to an active GitHub account.

2. After signing in, click the “+” icon in the top-right corner of the page. From the drop-down menu, select “New repository.”

3. In the “Create a new repository” page, enter “adf-test-repository” as the name of the repository.

4. Keep all the settings as is, and create the repository.

5. Once the repository is created, at the top of the repository page, in the navigation bar, click the “Branches” tab.

6. Now on the branches page, create a new branch called “adf-dev”. This will be the development branch for our data factory.

Refer to the images below to find step-by-step instructions on creating the new repository and development branch for the data factory in GitHub.