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dbt Documentation Freshness

A reading path through keeping dbt documentation accurate as your project evolves — from the case for automation to drift detection, coverage tracking, and a graduated rollout strategy

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dbtautomationdata quality

Notes on keeping dbt documentation accurate as a project evolves — CI enforcement, coverage tracking, drift detection, and a graduated automation strategy. For notes on generating documentation (scaffolding, AI tools, RAG), see the AI-powered documentation hub.

Reading Order

  1. Stale documentation is worse than missing documentation — Why stale descriptions that look complete cause more damage than obvious gaps.

  2. dbt Documentation CI Enforcement — Pre-commit hooks with dbt-checkpoint and CI tools (dbt-coverage, dbt-score, dbt-bouncer) that prevent new gaps from entering the project.

  3. dbt Project Evaluator for Documentation — How dbt-project-evaluator materializes documentation coverage as queryable models, and how dbt_meta_testing enforces folder-level documentation requirements.

  4. dbt documentation coverage tracking — Moving beyond pass/fail CI checks to track coverage trends over time. Includes dbt-coverage comparison workflows and dbt-score composite quality scoring.

  5. dbt documentation drift detection — Detecting when existing documentation falls out of sync with reality: column-level drift, git-based staleness signals, and schema drift for sources.

  6. dbt documentation automation strategy — A graduated rollout plan from a single pre-commit hook (any team, five minutes) to comprehensive automation with drift detection and AI remediation.