Why this matters
As a Data Platform Engineer, you build and govern shared data assets. Clear, consistent documentation is how teams discover datasets, trust them, and safely change them. It reduces incidents, speeds onboarding, and enables compliance.
- You will register datasets in a catalog with owners, SLAs, and lineage.
- You will write runbooks so on-call engineers can resolve pipeline incidents fast.
- You will record architectural decisions (e.g., PII handling, retention) for audit and future context.
- You will capture data contracts so producers and consumers align on schemas and expectations.
Who this is for
- Data Platform Engineers setting standards for multiple teams.
- Data Engineers contributing datasets to a central catalog.
- Analytics Engineers documenting models and quality guarantees.
- Platform/Infra peers who need runbooks and decision history.
Prerequisites
- Basic familiarity with data pipelines, schemas, and orchestration (e.g., batch scheduling).
- Understanding of data governance basics: ownership, PII, retention, access levels.
- Ability to read YAML/Markdown and write concise technical notes.
Concept explained simply
Documentation standards are agreed templates, naming, and rules for how we describe data assets and platform behavior. They make docs predictable and searchable. Think of them like API standards for humans: consistent inputs (templates), clear outputs (trustworthy understanding), and versioning.
Mental model
Use the 4L model: Locate, Learn, Leverage, Log.
- Locate: Catalog entries help people find the right dataset and owner.
- Learn: Specs and glossaries explain meaning, lineage, and quality.
- Leverage: Runbooks and how-tos help people use and operate assets.
- Log: Architecture Decision Records (ADRs) and change logs capture why things changed.
Standards you will use
Dataset Specification (for catalog entries)
Template fields to standardize:
- Name, Domain, Description (business-friendly)
- Owner (team), Steward (person/role)
- Schema (fields, types, description, nullable)
- Classifications (PII, PHI, Confidentiality)
- Quality (SLOs/SLA, checks, freshness)
- Lineage (sources, downstreams)
- Access (who can read/write), Retention
- Change policy (versioning, deprecation window)
Data Contract
A formal agreement between producer and consumer:
- Allowed schema changes and notice period
- Field-level guarantees (types, nullability, semantics)
- Delivery guarantees (frequency, delay tolerance)
- Error handling and incident escalation
Runbook (Operations)
- Symptoms, common causes, diagnostics, quick fixes
- Escalation path (on-call rotations)
- Rollbacks and safe retries
- KPIs and dashboards to check
Architecture Decision Record (ADR)
- Context, Decision, Options considered, Consequences
- Link to related tickets and affected datasets
- Version and date
Glossary and Naming
- One-line definitions for business terms
- Naming conventions: snake_case for fields, lower_snake for tables, prefix domains if needed
- Disallowed terms (ambiguous words)
Worked examples
Example 1: Dataset spec for "orders"
name: analytics.orders_daily
domain: analytics
owner_team: data-analytics
steward: jane.doe
classification: Confidential
retention: 25 months
freshness_sla: 2h from source close
schema:
- name: order_id
type: string
description: Unique order identifier
nullable: false
- name: customer_id
type: string
description: Hash of customer PII
nullable: false
- name: order_ts
type: timestamp
description: When the order was placed (UTC)
nullable: false
- name: total_amount
type: decimal(12,2)
description: Order total in USD
nullable: false
quality_checks:
- name: row_count_positive
rule: row_count > 0
schedule: daily
- name: fresh_within_sla
rule: max(order_ts) > now() - interval '2 hours'
lineage:
inputs: [raw.orders, dim.customers]
outputs: [mart.revenue_daily]
access:
readers: [bi-analysts, finance]
writers: [data-analytics]
change_policy:
versioning: semantic
deprecation_notice_days: 30
Example 2: Pipeline runbook snippet
service: orders_daily_etl
oncall: data-analytics-oncall
symptoms:
- Dashboard shows freshness breach > 2h
- Task orders_load failed with exit code 137
diagnostics:
- Check orchestrator logs for memory OOM
- Verify upstream raw.orders landed today
quick_fixes:
- Increase executor memory to 6GB and rerun failed task
- If raw missing, trigger upstream ingestion job
escalation:
- If fix fails twice, page platform-oncall
rollback:
- Revert to previous image tag v1.12.3
Example 3: ADR for PII hashing change
ADR-012: Switch customer_id hash from MD5 to SHA-256
Status: Accepted
Date: 2026-01-10
Context:
MD5 is considered weak; regulators require stronger hashing for PII.
Decision:
Use SHA-256 with per-tenant salt stored in KMS.
Options:
- Keep MD5 (rejected: weak)
- SHA-256 (chosen)
- Tokenization service (rejected: latency)
Consequences:
- Backfill required for 24 months history
- Downstream joins must use new hash
Change plan:
- Version field: customer_id_v2 (180-day deprecation for v1)
- Communicate via data-contract channel, 30 days notice
How to write good docs (fast)
Step 1: Choose the right template
Dataset spec, runbook, ADR, or data contract. Do not mix purposes.
Step 2: Fill must-have fields first
Owner, description, schema, classification, quality, lineage.
Step 3: Add change policy
State how changes are versioned and communicated.
Step 4: Make it scannable
Short sentences, bullets, and clear field descriptions.
Step 5: Validate
Self-check with the checklist below before publishing.
Publish checklist
- Asset has a clear owner and steward.
- Description is business-friendly and unambiguous.
- Schema lists types, nullability, and definitions.
- Classifications and retention are set.
- Quality checks and SLAs are stated.
- Lineage shows key inputs/outputs.
- Change policy and versioning are defined.
- Runbook exists for operational assets.
Exercises
Do these in your own editor. You can compare with example solutions below.
- Exercise 1 (ex1): Write a dataset spec for a "customers" table with PII fields, quality checks, and change policy.
- Exercise 2 (ex2): Draft an ADR to deprecate a column and introduce a versioned replacement with a safe rollout plan.
Common mistakes and self-check
- Missing owner: If an incident happens, who is paged? Add owner_team and steward.
- Vague descriptions: Replace "id" with "Unique identifier for X, stable across Y".
- No change policy: State how you version and how long you support old fields.
- Ignoring classification: Mark PII/PHI and specify retention/access.
- Lineage gaps: At least list primary upstreams and downstreams.
- Runbooks too short: Include diagnostics and rollbacks, not just "retry".
Self-check: Can a new teammate operate or use the asset with only your doc? If not, what would they ask you? Add those answers.
Practical projects
- Catalog sprint: Standardize 5 key datasets with complete specs and runbooks. Measure reduced incident resolution time after.
- Contract clinic: Create data contracts for two producer teams; run a schema-change drill and document the process.
- ADR week: Write ADRs for 3 recent decisions (storage format, partitioning, PII handling) and link them from dataset specs.
Learning path
- Learn the templates: dataset spec, runbook, ADR, data contract.
- Document one real dataset end-to-end.
- Add quality checks and SLAs; connect to monitoring.
- Introduce versioning and a change policy across assets.
- Practice incident runbooks and perform a rollback rehearsal.
Mini challenge
Your finance team wants to add a nullable field "discount_code" to orders. In 5 bullet points, outline the change policy, version impact, quality checks, and communication plan. Keep it under 120 words.
Next steps
- Apply the publish checklist to one dataset in your environment.
- Write one ADR for a recent platform decision and share with your team.
- Take the Quick Test below to validate your understanding.
Quick Test
Quick Test is available to everyone; only logged-in users have their progress saved.