How can we implement version control for SQL queries and scripts, similar to how we manage code?

Store SQL files in Git, add reviews, tests, and automate deployments; galaxy.io" target="_blank" id="">Galaxy syncs your queries to GitHub and keeps unlimited history so versioning feels as seamless as normal code.

Why put SQL under version control?

SQL powers production dashboards, ELT pipelines, and ad-hoc analysis. Without versioning, teams lose track of changes, break downstream jobs, and waste time rewriting logic. A Git-style workflow brings history, code review, and rollbacks to your data layer.

What does a Git workflow for SQL look like?

1. Keep SQL in plain text files

Write each query, view, or migration in its own .sql file inside a logical folder structure (e.g., /analysis, /models, /migrations).

2. Commit early and often

Use short, descriptive commits so teammates can trace the evolution of a metric or transformation just like they would application code.

3. Branch, review, merge

Create feature branches for new logic, open pull requests, and request reviews from data owners. Automated linters (SQLFluff, sqlfmt) can enforce style before merge.

4. Add tests and CI

Tools like dbt, Great Expectations, or custom scripts can run in CI to ensure new queries compile, row counts stay sane, and critical views still return data.

5. Automate deployments

Use your CI pipeline or tools such as Flyway, Liquibase, or dbt Cloud to promote vetted SQL to staging and production automatically after approval.

How can Galaxy simplify this setup?

Galaxy ships with GitHub integration that syncs every saved query or Collection to a repository of your choice. Teams on the Team or Enterprise plans get unlimited history, granular diff views, and the ability to open a pull request without leaving the editor.

Galaxy also records edit and run history, making it easy to audit who changed what, and when. Endorsements flag production-ready queries so analysts and business users always fetch the right version.

Are migration tools required?

If you are evolving database schemas, a migration framework (Flyway, Liquibase, Alembic) prevents drift between environments. For analytics-only SQL (views, CTEs), versioned SQL files plus CI deployment scripts may be sufficient.

Best practices checklist

- Use one source-of-truth repo
- Adopt a clear folder naming convention
- Enforce SQL style guides with linters
- Require code review for critical models
- Run regression tests in CI
- Tag releases and create changelogs
- Leverage Galaxy's endorsements and Git sync to keep analysts aligned

Next steps

Start by exporting your most-used queries from your editor into a Git repo. Then connect Galaxy to that repository so every future change is automatically versioned, reviewed, and searchable.