Documentation

Why documentation matters in Data Engineering

Documentation is how Data Engineers turn complex systems into reliable, understandable, and maintainable products. Clear docs reduce incidents, accelerate onboarding, and help analysts, ML engineers, and stakeholders use data safely. Good docs make your pipelines auditable, your schemas discoverable, and your team resilient when people rotate or on-call gets paged at 2 a.m.

• Fewer outages: Runbooks and ownership info speed up incident response.
• Better data trust: Data dictionaries and change logs prevent broken dashboards.
• Faster delivery: Source-to-target maps and metadata unblock reviewers and consumers.
• Scalability: Consistent templates let your platform grow without chaos.

Who this is for

• Early-career Data Engineers learning team-ready habits.
• Experienced engineers formalizing tribal knowledge.
• Analytics engineers and platform engineers who publish or operate pipelines.

Prerequisites

• Basic SQL and data modeling concepts (tables, schemas, lineage).
• Familiarity with pipelines/orchestrators (e.g., Airflow) and version control (e.g., Git).
• Comfort writing concise Markdown or lightweight YAML/JSON.

Learning path

1. Foundation: Decide standards
   Pick your documentation style and templates

   • Choose a consistent format (Markdown + YAML/JSON snippets).
   • Create templates for datasets, pipelines, and runbooks.
   • Define ownership fields (team, Slack channel, on-call rotation) and SLAs.
2. Dataset docs
   Write data dictionaries and metadata

   • Document purpose, freshness, partitioning, sensitive fields, and lineage.
   • Add column-level definitions and allowed values.
3. Pipeline docs
   Create runbooks and source-to-target (S2T) maps

   • Document triggers, dependencies, SLAs, retries, backfills, and failure modes.
   • Capture transforms and business logic in S2T maps.
4. Change management
   Set up versioned change logs

   • Record schema changes, deprecations, and impact analysis.
   • Tie docs to code reviews and release notes.
5. Consumer experience
   Onboarding guides and discoverability

   • Explain how to find datasets, request access, and interpret metrics.
   • Publish usage examples and query patterns.
6. Operational excellence
   Keep docs updated

   • Adopt a doc-as-code workflow (PRs, reviewers, checks).
   • Schedule doc audits and freshness indicators.

Worked examples

# dataset_orders.yaml
name: warehouse.orders
owner: data-platform
on_call: #data-oncall
sla:
  freshness: 2 hours
  availability: 99.9%
pii: false
partitions: [order_date]
lineage:
  upstream: [raw.orders_src, dim.customers]
  downstream: [marts.gmv_daily]
description: Orders placed on the ecommerce site.
columns:
  - name: order_id
    type: STRING
    description: Unique order identifier.
  - name: customer_id
    type: STRING
    description: References dim.customers.id
  - name: order_status
    type: STRING
    valid_values: [PLACED, SHIPPED, CANCELLED, RETURNED]
  - name: order_total
    type: NUMERIC(12,2)
    description: Total amount in USD; excludes shipping.

# Data Dictionary: warehouse.orders

## Purpose
Customer order facts used for revenue and fulfillment reporting.

## Columns
- order_id: Unique ID. Example: "A12345".
- customer_id: FK to dim.customers.id.
- order_status: Enum: PLACED|SHIPPED|CANCELLED|RETURNED.
- order_total: USD decimal, excludes shipping.
- order_date: UTC date of order placement.

## Constraints
- order_id unique per table.
- (customer_id, order_date) not null.

## Freshness
- Updated every 30 minutes by airflow_dag_orders.

target_table,target_col,source_table,source_expr,notes
warehouse.orders,order_id,raw.orders_src,src.order_id,Primary key
warehouse.orders,customer_id,raw.orders_src,src.cust_id,Map via dim.customers for SCD
warehouse.orders,order_status,raw.orders_src,UPPER(src.status),Normalize to uppercase
warehouse.orders,order_total,raw.orders_src,CAST(src.amount_usd AS NUMERIC(12,2)),Decimal format
warehouse.orders,order_date,raw.orders_src,DATE(src.created_at),UTC date

# Runbook: airflow_dag_orders

## Overview
Ingests raw.orders_src and builds warehouse.orders every 30 minutes.

## Triggers
- Schedule: cron(*/30 * * * *)
- Manual backfill supported (max 7 days).

## Dependencies
- raw.orders_src ingestion completed.
- dim.customers up to date.

## SLA
- Freshness: <= 2 hours end-to-end.
- Alert if > 4 consecutive failures.

## Failure Modes
- Upstream delay: backoff retry x3.
- Schema drift in raw: column additions allowed; removals break build.

## On-call Actions
1) Check Airflow logs for task: build_orders.
2) Validate upstream data volume vs 7-day baseline.
3) Re-run failed task or trigger backfill.
4) If schema drift, open PR to mapping + dictionary; tag #data-oncall.

# Changelog: warehouse.orders

## [1.4.0] - 2025-05-12
### Added
- order_status valid value RETURNED added.

### Deprecated
- order_state alias will be removed in 1.6.0.

### Impact
- Dashboards referencing order_state should migrate to order_status.

## [1.3.1] - 2025-03-02
### Fixed
- Corrected order_total rounding to 2 decimals.

# Onboarding: Orders Domain

## Access
- Request role: ANALYST_ORDERS via access request form.

## Start Here
- warehouse.orders for facts; dim.customers for attributes.

## Usage Examples
- Revenue by day: see sample query below.

sql
SELECT order_date, SUM(order_total) revenue
FROM warehouse.orders
GROUP BY order_date
ORDER BY order_date;

## Data Quality
- Expect 0-0.5% late-arriving orders; corrected within 24h.

## Support
- Ownership: data-platform. Slack: #orders-analytics.

Drills and exercises

Practice quickly. Use a scratch repo or folder and timebox each task to 15–20 minutes.

• Create a dataset template with fields: purpose, freshness SLA, owner, lineage, PII.
• Write a data dictionary for an existing table (≥5 columns).
• Draft a runbook for one pipeline, including three failure modes.
• Build a source-to-target CSV for a small transform (≥5 mappings).
• Add a CHANGELOG.md with Added/Changed/Deprecated sections.
• Define ownership and escalation path for one dataset.
• Add examples of safe queries (filters, limits, partitions).
• Mark PII fields and note handling rules.
• Add a “Last Reviewed” date to each doc.
• File a mock PR that updates docs along with a code change.

Common mistakes and debugging tips

• Out-of-date docs: Add “Last Reviewed” and align doc review with release cycles. Use PR templates that fail if docs weren’t updated.
• Ambiguous definitions: Use examples and constraints. Replace vague terms like “active user” with precise logic and time windows.
• Missing ownership: Every artifact needs an owning team, on-call, and a communication channel.
• No impact analysis: In change logs, state who/what might break and migration steps.
• Docs scattered: Keep a single source of truth. Cross-reference within the same doc set instead of duplicating.

Mini project: Document a small data mart

1. Pick a mart (e.g., Orders + Customers) with 2–3 tables.
2. Create dataset YAML for each table: owner, SLA, lineage, PII, partitions.
3. Write data dictionaries (≥6 columns per table) with examples and valid values.
4. Produce a source-to-target CSV for one transformation (raw to mart).
5. Draft a pipeline runbook: triggers, dependencies, failure modes, on-call actions.
6. Add a CHANGELOG.md and log one backward-incompatible change with migration notes.
7. Publish an onboarding guide with sample queries and access steps.
8. Review with a peer; apply feedback; mark “Last Reviewed” with date.

Practical projects

• Runbook library: Convert three existing pipelines into standardized runbooks. Add on-call actions and backfill steps.
• Catalog bootstrap: Write metadata files for your top five datasets, including PII flags and downstream consumers.
• Deprecation program: Identify an old table, write deprecation notice, impact, and migration plan; monitor adoption and cutover date.

Subskills

• Data Catalog And Metadata — Capture table purpose, lineage, freshness, PII, partitions, and owners in a consistent format.
• Pipeline Documentation And Runbooks — Describe triggers, SLAs, failure modes, and step-by-step on-call actions.
• Data Dictionaries — Define columns, types, valid values, constraints, and examples for clarity and trust.
• Source To Target Mapping — Specify how raw fields transform into modeled columns with expressions and notes.
• Change Logs And Versioning — Track Added/Changed/Deprecated items, impact analysis, and migration guidance.
• Onboarding Guides For Consumers — Explain access, dataset discovery, safe query patterns, and sample analyses.
• Defining Ownership And SLAs — Name responsible teams, contact paths, and measurable freshness/availability goals.
• Keeping Docs Updated — Use doc-as-code, PR templates, review cadences, and freshness indicators.

Next steps

• Adopt the provided templates in your repo; open a PR to add doc checks to your workflow.
• Run a monthly “Doc Day” to close gaps and update SLAs.
• Pair with an analyst to validate your definitions and examples.
• When ready, take the skill exam below to assess your readiness. Everyone can take it for free; logged-in users get saved progress.