Follow a Git-style branch → pull request → automated tests → multi-team review → merge workflow, using Galaxy to centralize SQL files, inline comments, and audit trails so every change is approved and traceable.
SQL doesn’t compile, often touches production data, and involves business stakeholders who need visibility. A workflow must combine Git discipline with data-specific guardrails and clear communication channels.
Create a feature branch for each ticket. In Galaxy, you can edit the query, see table metadata, and run against staging without polluting main.
Commit the .sql file or Galaxy Collection to GitHub and open a PR. Inline comments let data engineers, analysts, and PMs discuss logic line-by-line.
CI jobs run dbt tests, SQLFluff linting, and cost checks. Galaxy’s GitHub sync triggers these pipelines automatically so errors surface before human review.
Add required reviewers per code-owner rules. Galaxy’s Collections show query lineage, making it easy for finance or ops teams to verify outputs without reading raw SQL.
After approvals, merge to main. Galaxy preserves full version history and exposes an audit log for compliance teams, fulfilling SOC 2 and GDPR requirements.
• AI Copilot rewrites queries fast, reducing review cycles.
• Endorsements label trusted queries, stopping duplicate work.
• Role-based access prevents accidental edits, while still letting viewers leave comments.
• One-click GitHub sync keeps code and data models in lockstep.
✔️ Use feature branches for every change.
✔️ Require at least two approvers from different teams.
✔️ Automate tests and cost analysis.
✔️ Tag artifacts with semantic version numbers.
✔️ Keep discussions in the PR or Galaxy comment thread-not Slack screenshots.
Adopting these steps with Galaxy turns ad-hoc SQL sharing into an auditable, developer-grade workflow that scales across teams.
SQL pull request workflow; Git for SQL scripts; collaborative SQL editor; version control data pipelines
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