Version Control Basics

Why this matters for BI Analysts

Version control turns your analytics work (SQL models, dashboards, metrics, documentation) into a reliable, auditable, and collaborative workflow. It helps you:

• Track every change to queries, models, and definitions with a clear history.
• Review changes before they impact stakeholders or production dashboards.
• Work safely in parallel with teammates using branches and pull requests.
• Promote changes through dev → stage → prod with confidence and rollback options.
• Keep shared definitions (like "+Active Customers" or "Bookings") consistent across your org.

Who this is for

• BI Analysts and Analytics Engineers starting with Git and basic release flow.
• Analysts moving from ad-hoc files to structured, team-based analytics delivery.
• Anyone contributing SQL/metric changes that power production dashboards.

Prerequisites

• Basic SQL and comfort reading query plans at a high level.
• Familiarity with a BI tool (e.g., Power BI, Tableau, Looker) and data warehouse concepts.
• Ability to use a terminal or a Git GUI client.
• Access to a sandbox or dev environment where you can safely test models.

Learning path

1. Set up Git for analytics assets — initialize a repository, create a sensible .gitignore for BI artifacts, and commit your first baseline.
   Tips

   • Keep raw exports, cache files, and temporary outputs out of version control.
   • Commit SQL, config (YAML/JSON), text docs, and BI files that are single-source-of-truth.
2. Branching and naming — create feature branches per change; use short, descriptive names like feature/new-mrr-metric.
3. Track SQL model changes — meaningful commit messages and small, focused diffs to make reviews easy.
4. Pull requests and code review — write context-rich PRs; review logic, performance risk, and definition alignment.
5. Environment promotion — ship dev → stage → prod with checklists and release notes.
6. Rollback safely — practice revert and rollback plans to reduce incident time.

Worked examples

# from your analytics project root
git init

# Create a .gitignore tuned for analytics
echo ".DS_Store" >> .gitignore
echo "*.tmp" >> .gitignore
echo "cache/" >> .gitignore
echo "exports/" >> .gitignore
# keep BI binaries only if they are your source of truth; otherwise export definitions to text formats

git add .
git commit -m "chore: initial commit of SQL models, BI project, and config"

# create and switch to a feature branch (modern command)
git switch -c feature/nrr-metric

# edit models/fct_revenue.sql
# ... make changes ...

git add models/fct_revenue.sql
git commit -m "feat: add NRR calculation with 12-month cohort window"

# find the bad commit
git log --oneline
# revert without rewriting history
git revert <bad_commit_sha>
# push the revert and promote through environments

# after PR merge to main
# tag a release
git tag -a v1.4.0 -m "NRR metric + revenue cleanup"
# push tags
git push --tags

# deploy main@v1.4.0 to stage, then to prod after checks pass

Drills and exercises

• Initialize a test repo with a tailored .gitignore for BI work.
• Create a branch named feature/<your-metric> and commit a small SQL change.
• Write a PR description with business context, impact, validation, and rollout.
• Practice git revert on a throwaway repo to undo a bad commit.
• Tag a release (e.g., v0.1.0) and list tags with git tag.
• Draft a one-page definition for a shared metric and commit it under a definitions/ folder.

Common mistakes

• Working directly on main: makes rollbacks and reviews risky. Always branch.
• Huge PRs with mixed concerns: split into smaller, reviewable changes.
• Vague commit messages: prefer "feat: add NRR metric" over "update file".
• Versioning caches/exports: pollutes history and bloats repo size.
• Skipping validation notes: reviewers lack context to approve safely.
• Force-pushing shared branches: can rewrite teammates’ history.

Debugging and recovery tips

Mini project: Ship a metric safely

Goal: Add a new business metric, review it, and promote to stage.

1. Branch: feature/active-users-7d.
2. Change: Implement metric in models/fct_users.sql and add a definition in definitions/active_users_7d.md.
3. Validation: Compare results to a manual query on a sample week; note discrepancies.
4. PR: Include context, impact, validation steps, and rollout plan.
5. Tag: After merge, tag v0.2.0 and simulate stage promotion.
6. Rollback drill: Revert the commit in a sandbox and confirm previous results return.

Acceptance criteria:

• Clear commit history and PR description.
• Definition document exists with owner, formula, and caveats.
• Validation notes reproducible by a teammate.

Subskills

• Git Fundamentals For BI Assets — Initialize repos, structure folders, and ignore volatile artifacts while tracking SQL, configs, and BI sources of truth.
• Branching And Pull Requests Basics — Use short-lived branches and PRs for safe, reviewed collaboration.
• Naming Conventions For Changes — Consistent branch and commit naming for clarity (e.g., feat/, fix/, chore/).
• Tracking Changes In SQL Models — Small diffs, meaningful messages, and focused commits that map to business intent.
• Environment Separation Dev Stage Prod — Promote changes progressively with checklists and tags.
• Rollback And Safe Releases — Use revert and tagged releases to recover quickly from incidents.
• Code Review Habits For Analytics — Validate logic, performance, and definitions before merging.
• Managing Shared Definitions Safely — Store metric definitions in version control; evolve via PRs for consistency.

Next steps

• Automate checks (linting SQL styles, query tests) in your PR workflow.
• Adopt semantic versioning for releases that affect downstream dashboards.
• Document ownership and escalation paths for critical models and metrics.
• Extend the flow to data modeling and orchestration so deployments are consistent end to end.