Organize queries by domain, apply strict naming conventions, attach owner + purpose metadata, and leverage Galaxy Collections with endorsements to surface trusted logic in seconds.
A predictable layout trims search time, prevents duplicated work, and builds trust in results. When every query lives in one well-labeled place, engineers spend minutes-not hours-answering data requests.
Group first by business domain (billing, product, marketing), then by artifact type (metrics, ad-hoc, transforms). Inside each folder, use a kebab-case file name that answers “what does this return?”-e.g., active-users-daily.sql.
/product/metrics/active-users-daily.sql
/product/ad-hoc/feature-adoption-2025-q1.sql
/billing/transforms/events_to_invoices.sql
Embed a YAML or SQL header with owner, last_validated, datasource, and purpose. Store generated docs in the repo or an adjacent wiki so anyone can audit lineage quickly.
Use Git branching to propose changes, require code review for logic used in production dashboards, and tag releases so downstream jobs reference immutable commits. CI tests can even lint SQL style or check execution plans.
Galaxy makes structure tangible. With Collections, you mirror the folder scheme inside the editor, endorse gold-standard queries, and grant role-based run or edit rights. The SQL editor auto-suggests saved snippets as you type, and AI Copilot can refactor or document legacy scripts instantly. Enable GitHub sync to keep your repo and Galaxy workspace in perfect lockstep.
How do I organize SQL queries in Git?;Best practices for reusable query library;SQL query version control tips;How to document SQL ownership
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