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Keeping Docs Updated

Learn Keeping Docs Updated for free with explanations, exercises, and a quick test (for Data Engineer).

Published: January 8, 2026 | Updated: January 8, 2026

Table of Contents

Who this is for

Data Engineers and platform builders who maintain pipelines, datasets, orchestration, and operational runbooks and want documentation that stays accurate as systems evolve.

Prerequisites

  • Basic familiarity with your team’s code review process (pull requests or similar)
  • Comfort writing Markdown-style docs and updating READMEs/runbooks
  • Understanding of your data model (tables, columns, SLAs, lineage)

Why this matters

Out-of-date docs cause failed deployments, broken dashboards, and on-call stress. In real data engineering work you will:

  • Ship schema changes that affect downstream teams
  • Modify DAG schedules and SLAs
  • Deprecate datasets or rename columns
  • Hotfix jobs and adjust runbooks

Keeping docs updated prevents confusion, speeds onboarding, and reduces incidents.

Concept explained simply

Updated documentation is a habit plus a few guardrails. Treat docs like code: versioned, reviewed, and released with changes. When something changes in code, a corresponding doc change happens in the same PR or release.

Mental model

Think of your system as a contract between producers and consumers. Docs are the contract text. If the contract changes (schema, SLA, ownership), the contract must be reissued immediately. No reissue = broken trust.

Core practices (fast wins)

  • Definition of Done includes docs updated (DoD: code merged only when docs are updated or explicitly N/A)
  • Docs-as-code: store docs with the pipeline or dataset
  • Change triggers: ship a doc update whenever any of these change:
    • Table schema or column semantics
    • DAG schedule, SLA/SLO, alerting
    • Data quality checks or thresholds
    • Ownership (team/on-call) or escalation path
    • Dependencies/lineage or contracts
    • Runbook procedures for failures
  • Changelog: each dataset or pipeline keeps a human-readable changelog snippet per release
  • Ownership and timestamps: each doc shows owner and last-reviewed date

Worked examples

Example 1: Renaming a column

Change: orders.total_price becomes orders.gross_amount.

  • Docs to update: dataset README (column table), data dictionary entry, any example queries in docs
  • Add deprecation note: mention old name and removal date
  • Changelog line: “2026-02-12: total_price renamed to gross_amount; same calculation”
  • Runbook: add temporary mapping tip for downstream fixes
Example 2: SLA change for a daily DAG

Change: Job now completes by 06:00 UTC instead of 04:00 UTC.

  • Docs to update: pipeline README (Schedule & SLA section), consumer-impact note
  • Alert policy doc: adjust alert window
  • Changelog line: “SLA moved to 06:00 UTC; consumers adjust refresh expectations”
Example 3: Deprecating a dataset

Change: Replace legacy_events with events_v2.

  • Docs to update: legacy dataset doc gets a bold deprecation banner with EOL date
  • New dataset doc: includes migration guide (field mapping)
  • Changelog lines on both datasets
  • Runbook: add note for on-call on how to respond to legacy failures during wind-down

Step-by-step: Make updates part of delivery

  1. Before merging: add a “Docs impact” checklist to PR template
  2. Create/modify doc files in the same PR: README, schema table, runbook, changelog
  3. Tag owners for review: code reviewer checks docs updated, not just code
  4. On merge: ensure version/release notes include doc changes
  5. Weekly: 15-minute doc health scan (spot stale timestamps, missing owners)
Copy-paste PR checklist
  • [ ] Docs impact reviewed
  • [ ] Dataset fields updated (added/removed/renamed/semantics)
  • [ ] Schedule/SLA and alerting updated
  • [ ] Runbook steps updated
  • [ ] Changelog entry added
  • [ ] Owner and last-reviewed updated

Templates you can reuse

Dataset README skeleton
# Dataset: <name>
Owner: <team/contact>
Last reviewed: <YYYY-MM-DD>
Purpose: <1-2 sentences>
Schedule/SLA: <e.g., daily by 06:00 UTC>
Freshness expectation: <e.g., <24h>
Lineage: <upstream> -> <this> -> <downstream>
Schema:
| Column | Type | Nullable | Description |
|--------|------|----------|-------------|

Quality checks: <list with thresholds>
Known caveats: <edge cases>
Deprecations/changes: <summary or link to changelog section>
Runbook snippet
# Runbook: <pipeline>
Owner: <team/contact> | Escalation: <on-call>
Last reviewed: <YYYY-MM-DD>
Symptoms: <alerts/messages>
Quick checks: <commands/queries>
Common causes: <ordered list>
Fix steps: <numbered steps>
Rollback plan: <how>
Customer impact: <who/what>

Common mistakes and self-check

  • Mistake: Updating docs quarterly. Fix: Update in the same PR as the change.
  • Mistake: Docs without owner. Fix: Add Owner to every doc header.
  • Mistake: No dates. Fix: Include Last reviewed and bump on each check.
  • Mistake: Hidden changes. Fix: Keep a short, human-readable changelog.
  • Mistake: Only success-path docs. Fix: Maintain runbooks for failure modes.
Self-check mini audit (5 minutes)
  • Every dataset doc has Owner and Last reviewed
  • Latest code change has a matching doc change
  • SLAs and schedules match actual orchestration
  • At least one runbook exists for critical pipelines
  • Changelog entries exist for last two releases

Exercises

Do these to build the habit. The solutions are collapsible; try first, then peek.

Exercise 1: Turn a change into doc updates (mirrors ex1)

Scenario: A daily job for table user_sessions moved from 03:00 to 05:30 UTC; a new column device_type was added. Write the doc updates you would make.

Hints
  • Think: README sections affected
  • SLA, schema, changelog, runbook
Show solution

See the solution in the Exercises panel below (ex1).

Exercise 2: Create a PR checklist (mirrors ex2)

Draft a 6–8 line checklist that forces doc updates for any pipeline change.

Hints
  • Cover schema, SLA, quality checks, runbooks
  • Include owner/date fields
Show solution

See the solution in the Exercises panel below (ex2).

Practical projects

  • Project 1: Add a Docs Impact section to your PR template and pilot it on one pipeline for two weeks
  • Project 2: Migrate one dataset doc to the provided template, fill all fields, and backfill its last 5 changelog entries
  • Project 3: Run a 30-minute docs audit on your top 3 pipelines; fix missing owner/date and mismatched SLAs

Learning path

  1. Start: Add Owner and Last reviewed to all critical docs
  2. Adopt: Add a Docs impact checklist to PRs
  3. Ritual: Weekly 15-minute doc health scan
  4. Improve: Keep per-dataset changelogs
  5. Automate: Add simple CI checks (e.g., require Owner field present) — optional but helpful

Next steps

Pick one pipeline or dataset and bring its docs to green today: owner set, last reviewed updated, SLA accurate, schema correct, and a fresh changelog entry.

Mini challenge

In under 20 minutes, update one runbook to include: symptoms, quick checks, and a one-step rollback. Mark the doc with today’s date.

Quick Test info

Take the quick test below to check your understanding. Anyone can take it; logged-in users will have their progress saved automatically.

Practice Exercises

2 exercises to complete

Instructions

Scenario: A daily job for table user_sessions moved from 03:00 to 05:30 UTC, and a new column device_type (STRING) was added describing the user’s device category. List the exact documentation updates you would make across README, schema table, runbook, and changelog. Use concise bullet points.

Expected Output
- README Schedule/SLA updated to 05:30 UTC; consumer note added - Schema table: + device_type STRING with description; nullable flag - Runbook: add alert window and device_type backfill note (if any) - Changelog entry with date and two bullets (SLA change, new column) - Last reviewed updated to today; Owner unchanged

Keeping Docs Updated — Quick Test

Test your knowledge with 8 questions. Pass with 70% or higher.

8 questions70% to pass
Change Logs And VersioningOnboarding Guides For Consumers

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