Commit Hygiene And Messages

Why this matters

Clear, small, well-labeled commits make analytics work safe and reviewable. As an Analytics Engineer, you will:

• Refactor SQL models without breaking downstream dashboards.
• Add data quality tests and document model changes for teammates.
• Hotfix pipeline issues quickly and explain the impact.
• Audit how a metric changed last week and why.

Good commit hygiene cuts review time, reduces rollbacks, and makes on-call debugging calmer.

Concept explained simply

A good commit is a small, focused change with a message that explains what changed and why. The subject is a short command; the body gives context and how you verified it.

Mental model

Think of each commit as a single puzzle piece. Each piece should be understandable and removable on its own. If removing one commit would partially break unrelated work, the commit is too big or mixed.

Commit hygiene: what good looks like

• One logical change per commit (atomic, reversible).
• Small scope: refactor, feature, test, docs, or config each in their own commit when possible.
• Subject line in imperative mood (e.g., Add, Fix, Update) about 50 characters.
• Blank line after subject; wrap body near 72 characters per line.
• Body covers: what changed, why, impact, validation, and rollback notes.
• Reference relevant model names and tables (e.g., models/stg_orders.sql).
• Exclude generated or large artifacts (compiled targets, data extracts).
• Make noisy formatting changes in a separate commit from logic changes.

Templates and conventions

Common subject prefixes you may see in analytics repos: model:, test:, docs:, seed:, refactor:, fix:, chore:. Use them only if your team agrees.

Subject line (imperative, ~50 chars)

What changed:
- Brief bullet(s)

Why:
- Business or technical reason

Impact:
- Downstream models/dashboards/SLAs affected

Validation:
- How you tested (e.g., dbt tests, sample queries, row counts)

Rollback:
- How to revert or toggle if needed

Refs:
- Ticket or incident ID if applicable

Worked examples

Good
Subject: test: add not_null on fct_orders.order_id

What changed:
- Added not_null test for order_id in fct_orders.yml

Why:
- Recent nulls caused dashboard drop-offs

Impact:
- Build will fail earlier if nulls reappear

Validation:
- dbt test passed locally; 0 failures in staging

Rollback:
- Revert the yaml change

Poor
Subject: fixes
Body: added stuff for orders

Good
Subject: refactor: simplify join in stg_transactions

What changed:
- Replaced subquery with window function

Why:
- Reduce scan cost and runtime

Impact:
- Same business logic; downstream models unaffected

Validation:
- Row count and key metrics match last prod run
- Query runtime: 3m -> 1m on staging

Rollback:
- Revert models/stg_transactions.sql

Poor
Subject: change sql
Body: perf tweak

Good
Subject: fix: correct churn_flag in dim_customers

What changed:
- Adjusted CASE to treat reactivations correctly

Why:
- Monthly churn was overstated by ~2pp

Impact:
- Affects churn dashboard and retention KPI

Validation:
- Compared 30-day cohort before/after; differences expected
- Added unit test for reactivation scenario

Rollback:
- Revert dim_customers.sql; remove new test

Poor
Subject: update dim_customers
Body: fixing logic

Practical steps you can follow today

1. Stage intentionally: use selective staging (git add -p) to keep commits atomic.
2. Write subject first: if it is too long, your commit is likely too big.
3. Fill the template: add what/why/impact/validation/rollback.
4. Self-check: can you revert this commit without touching others?
5. Push and open a focused PR: one coherent story per PR.

Exercises

These mirror the exercises below so you can practice and then check your work.

1. Exercise ex1: You implemented three changes locally: (1) created models/stg_transactions.sql; (2) updated models/dim_customers.sql to fix churn_flag; (3) added a not_null test for id in dim_customers.yml. Write three separate commit messages using the template.

Exercise self-check checklist

• Each commit contains exactly one logical change.
• Subject uses imperative mood and is concise.
• Body states what changed and why.
• Impact names affected models/dashboards.
• Validation describes concrete checks or tests.
• Rollback mentions how to revert.

Common mistakes and how to self-check

• Mixed commits: logic + formatting + tests together. Fix by splitting and using separate commits.
• Vague messages: 'update stuff' provides no audit trail. Fix by stating what and why.
• No validation: skipping data checks. Add row counts, dbt tests, or sample query comparisons.
• Including generated artifacts: committing compiled targets or CSV extracts. Use .gitignore and stage intentionally.
• Shallow subjects: subject only repeats filename. Make it outcome-oriented.
• Overlong commits: if your subject needs a paragraph, split the change.

Mini challenge

Take one of your recent PRs. Recreate it as a sequence of 2–5 atomic commits. For each commit, draft a message with what/why/impact/validation/rollback. Could a teammate review each commit in isolation?

Who this is for

• Analytics Engineers and BI Developers who collaborate via Git.
• Data Analysts transitioning to analytics engineering.
• Anyone contributing SQL/ELT/dbt in a shared repo.

Prerequisites

• Basic Git operations: clone, add, commit, push, pull.
• Comfort editing SQL or dbt models.
• Ability to run tests or simple validations (e.g., dbt test).

Learning path

1. Commit hygiene and messages (this lesson).
2. Branching and pull requests.
3. Code reviews for data changes.
4. Rebasing and squashing safely.
5. Release tags and change logs for analytics repos.

Practical projects

• Adopt a commit template in your analytics repo and enforce it via Git config.
• Refactor one model into smaller steps; commit each step with clear validation notes.
• Add a missing dbt test, fix a flaky test, and document the impact in the commit.

Next steps

• Apply the template on your next feature branch.
• Pair with a teammate to review each other’s commit messages for a week.
• Standardize prefixes (e.g., model:, test:, docs:) for your team.

Quick Test and progress

Take the quick test below to check your understanding. The test is available to everyone; only logged-in users get saved progress and scores.