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Automated Documentation Practices

Learn Automated Documentation Practices for free with explanations, exercises, and a quick test (for Data Architect).

Published: January 18, 2026 | Updated: January 18, 2026

Table of Contents

Why this matters

As a Data Architect, you are responsible for making data assets discoverable, trustworthy, and easy to govern. Automated documentation reduces manual effort, prevents drift between reality and docs, and gives stakeholders reliable visibility into schemas, lineage, ownership, data quality, and SLAs.

  • Onboarding: New engineers find table purpose, owners, and sample queries instantly.
  • Impact analysis: Accurate lineage shows which downstream reports break when a column changes.
  • Compliance: PII tags, retention rules, and access classifications are applied and visible consistently.
  • Operations: CI/CD gates fail early if required metadata (like descriptions or owners) is missing.

Note: The quick test on this page is available to everyone; only logged-in users have their progress saved.

Who this is for

  • Data Architects designing metadata and lineage strategies.
  • Data Engineers and Analytics Engineers maintaining pipelines.
  • Platform Engineers enabling cataloging and governance.

Prerequisites

  • Comfort with SQL and data warehouse concepts (schemas, tables, views).
  • Basic understanding of ETL/ELT pipelines and orchestration.
  • Familiarity with version control (e.g., git) and CI basics.

Concept explained simply

Automated documentation is the practice of generating and updating documentation directly from your systems and code. Instead of writing wiki pages by hand, you pull facts from the warehouse (schemas, column types), pipelines (lineage), tests (quality status), and configs (owners, SLAs). You publish and refresh this on every change or run.

Mental model

Think of your data platform as a living organism with sensors:

  • Sensors collect signals: INFO_SCHEMA, pipeline definitions, SQL, configs, test results.
  • A small brain organizes the signals into a standard model: assets, columns, owners, tags, lineage edges.
  • Publishers render it: searchable pages, READMEs, YAML/JSON contracts, diagrams, badges.

When the organism changes (new column, failed test), sensors detect it and publishers refresh the docs automatically.

Core building blocks

  • Doc-as-code: store generated docs alongside code and version them.
  • Metadata harvesters: pull schemas, constraints, tags, and sample stats from the platform.
  • Lineage extractors: parse SQL and pipeline graphs to map source-to-target relationships.
  • Contracts & policies: YAML/JSON for owners, SLAs, classifications, retention.
  • Validation gates: CI checks for required fields and drift.
  • Renderers: simple templates to produce HTML/Markdown/YAML for humans and machines.

Worked examples

Example 1: Generate table and column docs from your warehouse

Goal: Build a nightly task that reads schema metadata and outputs a human-friendly summary.

  1. Query INFO_SCHEMA (or catalog APIs) to list tables and columns, including types, nullability, comments, and last altered time.
  2. Enrich with owners and classifications from a YAML file (e.g., team, PII flag, retention).
  3. Render an output file per table (Markdown or HTML) with sections: Purpose, Owner, Columns, Partitions/Clustering, Sample query.
  4. Commit artifacts to the repo or publish to your internal docs site.

Tip: If a column has no description, render a warning badge and fail CI for new additions.

Example 2: Extract lineage from SQL transformations

Goal: Parse transformation SQL to identify upstream sources and column-level mappings.

  1. Collect SQL for each model/view.
  2. Normalize SQL (remove comments, expand CTEs, resolve aliases).
  3. Extract FROM/JOIN sources and map target columns to source expressions.
  4. Publish lineage edges (source_table -> target_table) and optionally column-level lineage.

Tip: Treat subqueries and CTE chains carefully; resolve aliases to avoid false edges.

Example 3: Documentation gates in CI

Goal: Prevent merges that degrade documentation quality.

  1. On pull request: run metadata harvester on changed models only.
  2. Run a policy check: every new table must have owner, description, and classification tags.
  3. Fail if policy violations exist; output a clear report showing missing fields.
  4. On main branch: regenerate full documentation and publish it.

Tip: Allow a lightweight human-readable overlay (notes.md) while keeping generated sections read-only.

Learning path

  1. Start with doc-as-code: decide where generated artifacts live and how they are versioned.
  2. Automate schema docs: harvest INFO_SCHEMA and render per-table pages.
  3. Add lineage extraction: parse SQL/pipeline graphs and publish edges.
  4. Introduce metadata policies: owners, descriptions, PII classification; enforce in CI.
  5. Extend with data contracts: JSON/YAML schemas and example payloads.
  6. Surface data quality: show last run status, test counts, and freshness.
  7. Harden and scale: incremental updates, idempotent jobs, and change detection.

Checklist: Good automated docs

  • Every asset shows Owner, Domain, and Purpose.
  • Columns have type, nullability, and clear descriptions.
  • Lineage edges are present and up to date.
  • PII/classification tags and retention are visible at table/column levels.
  • Data quality badges indicate last run status and freshness.
  • Docs regenerate on code or schema change and after pipeline runs.
  • CI fails when required metadata is missing.
  • Human overlays exist but never overwrite generated sections.

Exercises

Do these in a scratch environment or on paper. They mirror the exercises below the article and include solutions.

Exercise 1: Generate schema docs

Write a SQL query (or pseudocode) that extracts table, column, data type, is_nullable, and column comment/description for a given schema. Then outline a simple Markdown template your generator would fill per table, including an Owner and Classification field pulled from a YAML map.

Hint

Use your platform's information schema views. Join columns to tables, and coalesce empty comments to a placeholder like "TODO: add description".

Exercise 2: Parse lineage from SQL

Given this SQL, list upstream tables and create a mapping from target columns to source expressions:

CREATE OR REPLACE VIEW marts.daily_revenue AS
WITH orders AS (
  SELECT o.order_id, o.customer_id, o.order_ts::date AS order_date, o.total_amount
  FROM raw.orders o
  WHERE o.status = 'COMPLETE'
),
line_items AS (
  SELECT li.order_id, SUM(li.price * li.quantity) AS line_total
  FROM raw.line_items li
  GROUP BY li.order_id
)
SELECT o.order_date,
       SUM(li.line_total) AS revenue
FROM orders o
JOIN line_items li ON li.order_id = o.order_id
GROUP BY o.order_date;
Hint

Identify CTEs and their sources, then the final SELECT target. Upstream real sources are in raw.* tables.

  • Self-check after each exercise: Can a machine run your steps on every change without manual edits?
  • If the input SQL changes (e.g., new column), will your output update automatically?

Common mistakes and how to self-check

  • Mixing manual edits into generated files. Self-check: Generated files should be reproducible from scratch; store human notes separately.
  • Regex-only SQL parsing. Self-check: Verify against queries with nested CTEs and subqueries; add alias resolution.
  • Docs not tied to change triggers. Self-check: Ensure CI runs on PRs and a scheduled/after-run job updates prod docs.
  • Ignoring ownership. Self-check: No asset should lack an owner/team field; fail builds if missing.
  • Letting descriptions go stale. Self-check: Show a "stale" badge when last updated date exceeds a threshold.
  • Over-documenting everything. Self-check: Focus first on curated domains and critical pipelines.

Practical projects

  • Build a schema harvester that outputs per-table Markdown with owners and PII flags.
  • Create a lineage extractor that parses SQL models and emits a simple JSON edge list.
  • Implement a CI policy check that blocks merges when required metadata is missing.

Next steps

  • Add data quality badges based on your testing framework results.
  • Adopt data contracts: define field-level schemas and sample payloads, generate docs from them.
  • Introduce term harmonization: tag columns with business glossary terms and surface definitions.

Mini challenge

Pick one critical dataset. In one day, automate: (1) owner + purpose, (2) schema with column descriptions, (3) at least two lineage edges, (4) a freshness badge. Deliver a single generated page. Measure how many manual steps remain and eliminate them next.

Practice Exercises

2 exercises to complete

Instructions

Write a SQL query (or pseudocode for your platform) to extract metadata for all columns in a target schema: table_name, column_name, data_type, is_nullable, and column_comment/description.

Then draft a Markdown template your generator would fill per table including:

  • Owner (from a YAML map like team_by_table)
  • Classification (e.g., PII: yes/no)
  • Columns table with name, type, nullable, description
  • Sample query showing top 5 rows
Expected Output
A SQL statement that selects from information schema views (or catalog equivalents) and a Markdown template string with placeholders like {{table_name}}, {{owner}}, and a loop over columns.

Automated Documentation Practices — Quick Test

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

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