Version Control (Data)

Version control systems (like git) track changes to code files: recording what changed, who changed it, when, and why. In data contexts, version control typically applies to code (SQL, Python, dbt models) rather than data itself. Transformation code, metric definitions, and pipeline orchestration are version-controlled; the data they produce is typically not. Version control enables: reviewing changes before merging (pull requests), reverting problematic changes (rollback), understanding change history, and collaborating safely (parallel changes to different files).

Version control emerged because data work is increasingly code-based. Rather than modifying spreadsheets or dashboards directly, teams write code (dbt, Python, SQL) that produces results. This code benefits from version control: you can see who changed what and when, revert bad changes, and review before merging. Version control also creates audit trails: demonstrating that changes were approved and tracked, useful for compliance.

Version control includes tools (git platforms like GitHub), workflows (branches for features, pull requests for review), and practices (commit messages explaining why, atomic changes). Organizations establish standards: feature branches, review requirements, and merge policies. Version control also enables other tools: CI/CD pipelines run tests when code changes, automated lineage tracking extracts from versioned models, and documentation generation happens automatically. Version control is foundational to DataOps.