How do you manage versioning of ETL scripts and workflows?

Managing versioning for ETL scripts and workflows involves using version control systems (VCS) like Git, structured repository organization, and clear processes for tracking changes. The primary goal is to maintain a reliable history of modifications, enable collaboration, and ensure reproducibility. Developers typically store scripts, configuration files, and documentation in a Git repository, leveraging branching strategies (e.g., feature branches, main/production branches) to isolate changes. For example, a team working on a data transformation script might create a feature branch for adding a new data source, test it in isolation, and merge it into the main branch after code review. Commit messages should explicitly describe changes (e.g., “Fix date parsing bug in sales data pipeline”) to provide context for future troubleshooting.

Repository structure plays a critical role in effective versioning. A well-organized ETL project might include directories like /scripts (for SQL or Python code), /configs (environment-specific settings), and /docs (data lineage or schema diagrams). Version tags (e.g., v1.2.0) help mark stable releases, while naming conventions like transform_customer_v2.py clarify iterations. For instance, if a bug is discovered in a production workflow, developers can check out the previous tagged version to roll back quickly. Testing environments (e.g., staging) should mirror production to validate changes before deployment. Automated testing pipelines can run sanity checks on pull requests to prevent breaking changes from merging into the main branch.

Handling dependencies and environment-specific configurations is equally important. Tools like Docker containerize ETL workflows to ensure consistent execution across environments, while configuration management tools (e.g., Apache Airflow’s Variables or Kubernetes ConfigMaps) separate environment-specific settings (e.g., database URLs) from code. For example, a Dockerfile might specify Python 3.10 and required libraries, ensuring all developers use the same runtime. Data versioning tools like DVC can track changes to input datasets, linking them to specific script versions. CI/CD pipelines (e.g., GitHub Actions) automate deployment, running tests and deploying to production only after changes pass predefined criteria. This combination of VCS, structured workflows, and environment management reduces errors and simplifies auditing.