Change Logs And Versioning

Why this matters

As an ETL Developer, you change pipelines, schemas, and schedules. Without clear change logs and versioning, teammates can’t trust what’s deployed, on-call engineers can’t diagnose incidents, and auditors can’t trace data lineage. Good versioning and change logs help you:

• Communicate what changed, when, and why
• Assess impact before a release and plan migrations
• Roll back quickly if something breaks
• Hand over stable, understandable systems

Concept explained simply

Change logs are chronological notes of what changed in your data systems. Versioning is a consistent way to label releases (like 1.4.0) so everyone speaks the same language about which code, schema, or config is running.

Mental model

Think of your pipelines as a train line. Versions are numbered stations on a line. The change log is the diary at each station: what was added, removed, fixed, and how to travel safely from the previous station (migration steps and rollback).

Core conventions

• Keep a single CHANGELOG per pipeline or data product. Newest entries at the top.
• Use consistent categories: Added, Changed, Deprecated, Removed, Fixed, Security.
• Tag releases: include pipeline or domain name, e.g., etl-orders-1.5.0.
• Version everything that can break consumers: code, configuration, and schemas.
• Include impact assessment, migration steps, and rollback notes in each entry.

## [1.5.0] - 2026-01-11
### Added
- New optional column: customer_tier (STRING). Default 'standard'.

### Impact
- Backward-compatible. Downstream queries unaffected unless they reference new column.

### Migration
- No action required. Optional: update BI dashboards to display customer_tier.

### Rollback
- Revert tag etl-orders-1.5.0 to 1.4.3. Dropped column will be ignored by consumers.

Worked examples

Example 1 — Non-breaking enhancement (MINOR)

Scenario: Add nullable column customer_tier to orders_dim.

• Version: 1.4.0 → 1.5.0 (MINOR)
• Change log: Category Added
• Migration: Backfill NULL to 'standard' default; no consumer changes needed
• Rollback: Drop column if reverting

## [1.5.0] - 2026-01-11
### Added
- Column customer_tier (STRING, nullable) to orders_dim. Default 'standard'.
### Impact
- Backward-compatible.
### Migration
- Backfill default value for existing rows.
### Rollback
- Drop column and revert tag.

Example 2 — Breaking rename (MAJOR) with safe rollout

Scenario: Rename column total to order_total in fact_orders.

• Version: 1.5.0 → 2.0.0 (MAJOR)
• Strategy: Dual-run period

1. Phase A (1.6.0): Add new column order_total; keep total; write both.
2. Phase B (1.7.0): Communicate deprecation of total; consumers migrate.
3. Phase C (2.0.0): Remove total; keep order_total.

## [2.0.0] - 2026-01-11
### Removed
- Column total removed from fact_orders (use order_total).
### Impact
- Breaking: downstream queries must use order_total.
### Migration
- Replace total with order_total in queries and transforms.
### Rollback
- Reintroduce total from order_total snapshot; revert tag to 1.7.0.

Example 3 — Hotfix (PATCH)

Scenario: Deduplication bug doubled yesterday’s rows.

• Version: 2.0.0 → 2.0.1 (PATCH)
• Change log: Category Fixed
• Migration: Run cleanup task to remove duplicates

## [2.0.1] - 2026-01-11
### Fixed
- Corrected deduplication window to 24h; removed duplicate rows from 2026-01-10.
### Impact
- Backward-compatible. Metrics may shift slightly due to corrected counts.
### Rollback
- Revert code to 2.0.0 if needed. Data rollback: restore partition snapshot for 2026-01-10.

How to work with change logs and versions (step-by-step)

1. Draft the change: describe intent, risk, and consumer impact.
2. Decide version bump: PATCH, MINOR, or MAJOR.
3. Plan safety: backfill, validation queries, snapshot/backup, rollback path.
4. Write the change log entry before release: Added/Changed/etc., Impact, Migration, Rollback.
5. Tag and release: use consistent tag naming (e.g., etl-orders-1.6.0).
6. Announce: share summary and migration notes with stakeholders.
7. After release: verify metrics, update documentation, close the entry with results if needed.

Common mistakes and self-check

• Mistake: Only describing code changes, not data impact. Fix: Always include Impact, Migration, and Rollback.
• Mistake: Skipping minor/patch bumps. Fix: Version every release; small doesn’t mean optional.
• Mistake: Breaking change with no transition period. Fix: Use dual-write/dual-read or compatibility layers.
• Mistake: Oldest-first logs. Fix: Newest entries on top.
• Mistake: Inconsistent naming. Fix: Standardize tags and file names.

Self-check: Can a new teammate recover the last stable version and know exactly how to migrate? If not, your entry is missing steps.

Exercises

Do these now. You can compare with the solutions. Then take the Quick Test. Note: The test is available to everyone; only logged-in users get saved progress.

1. Version bump + changelog — Decide the bump and write a clear entry for an index addition on users_dim.email (performance only).
2. Plan a safe breaking change — You must split column full_name into first_name and last_name in customers_dim. Outline release phases, migration, and rollback.
3. Name your migration set — Propose a versioned folder/file naming scheme for schema changes for the payments pipeline.

Version: (PATCH/MINOR/MAJOR)
Tag: (pipeline-name-version)

## [X.Y.Z] - YYYY-MM-DD
### (Added/Changed/Deprecated/Removed/Fixed/Security)
- (What changed)

### Impact
- (Who is affected and how)

### Migration
- (Exact steps)

### Rollback
- (Exact steps)

Practical projects

• Create a sample repo with two pipelines (orders, customers). Implement three releases: 1) MINOR add column, 2) PATCH bugfix, 3) MAJOR rename with dual-write. Maintain CHANGELOGs and tags.
• Design a schema evolution plan for a data mart (sales). Include deprecation timelines and a migration calendar.
• Build a validation checklist: pre-release and post-release SQL queries and expected thresholds.

Who this is for

• ETL Developers who release pipelines and schemas
• Data Engineers responsible for reliability and audits
• Analytics Engineers coordinating downstream dashboards

Prerequisites

• Basic Git usage (commit, tag)
• Comfort with SQL and schema changes
• Understanding of downstream data consumers (BI, ML, APIs)

Learning path

1. Start with semantic versioning basics (this lesson).
2. Practice writing full CHANGELOG entries with impact/migration/rollback.
3. Simulate a MAJOR change using a dual-write rollout.
4. Adopt a consistent migration file naming scheme.
5. Add automated validation queries to your release checklist.

Next steps

• Apply versioning and change logs to one real pipeline.
• Review with a teammate and refine your templates.
• Automate tag creation and CHANGELOG checks in your CI later.

Mini challenge

You need to drop column legacy_id in products_dim. Propose a two-phase plan that avoids breaking dashboards and write the final 2.0.0 CHANGELOG entry with a clear rollback.

Quick Test

Ready? Take the quick test below. You can do it for free. Logged-in users will have results saved to their profile.