Documentation And Handover

Why this skill matters for ETL Developers

Great ETL isn’t done until others can run, support, and safely change it. Documentation and handover make your pipelines reliable in production, speed up onboarding, reduce outages, and preserve knowledge when team members rotate.

• Prevents rework by clarifying requirements, mappings, and assumptions.
• Reduces incidents with clear runbooks and diagrams.
• Enables smooth support and faster recovery when jobs fail.
• Builds trust with stakeholders via traceable sign-offs and change logs.

What you’ll be able to do

• Create Source-to-Target (S2T) mappings that specify transformations and keys.
• Write a precise data dictionary with business definitions and technical types.
• Draw job flow diagrams that reflect schedules and dependencies.
• Prepare operational runbooks with start/stop, recovery, and SLAs.
• Maintain change logs and versioning so releases are auditable.
• Produce onboarding notes for L1/L2 support teams.
• Capture known limitations and assumptions.
• Run a stakeholder sign-off process with clear acceptance criteria.

Who this is for

• ETL Developers shipping or maintaining production pipelines.
• Data engineers who need reusable, supportable data flows.
• Analyst engineers documenting transformations and lineage.

Prerequisites

• Basic SQL and ETL concepts (sources, staging, transformations, loads).
• Familiarity with your orchestration tool (e.g., cron, Airflow, dbt jobs, native scheduler).
• Basic understanding of version control (e.g., Git).

Learning path (practical roadmap)

1. Start with the S2T mapping. Define grain, primary keys, transformations, filters, and target table structure.
2. Add a data dictionary. Clarify field meanings, data types, nullability, allowed values, and business rules.
3. Draw job flows. Show dependencies, triggers, schedules, retries, and alerts.
4. Write an operational runbook. Provide run/stop/retry steps, parameters, SLAs, and escalation paths.
5. Establish change log and versioning. Record what changed, why, who approved, and release version.
6. Prepare onboarding notes. Summarize how support teams monitor and triage.
7. List limitations and assumptions. Capture what the pipeline does not do and known caveats.
8. Run sign-off. Define acceptance tests, gather approvals, and archive the package.

Worked examples

SOURCE: oltp.orders (daily increment, watermark on updated_at)
TARGET: dw.fct_orders (grain: order_id)

KEYS
- PK (target): order_id
- Natural key (source): order_id

TRANSFORMATIONS
- order_date := CAST(order_timestamp AS DATE)
- revenue_usd := amount * fx_rate (from dim_fx on date)
- status := UPPER(TRIM(status))
- customer_sk := DIM_CUSTOMER_LOOKUP(source.customer_id)

FILTERS
- WHERE is_test = false AND order_timestamp >= :watermark

ERROR HANDLING
- Invalid FX: send to quarantine table dw.err_orders with reason

table: dw.fct_orders
owner: data-eng
fields:
  - name: order_id
    type: BIGINT
    business_definition: Unique order identifier
    nullable: false
  - name: order_date
    type: DATE
    business_definition: Calendar date of order
    nullable: false
  - name: revenue_usd
    type: NUMERIC(12,2)
    business_definition: Order revenue converted to USD
    nullable: false
    calculation: amount * fx_rate on order_date
  - name: status
    type: VARCHAR(20)
    business_definition: Order lifecycle status
    allowed_values: ["NEW","PAID","SHIPPED","CANCELLED","REFUNDED"]

from airflow import DAG
from airflow.operators.empty import EmptyOperator
from datetime import datetime

dag = DAG(
    dag_id="orders_daily",
    start_date=datetime(2024,1,1),
    schedule_interval="0 2 * * *",
    max_active_runs=1,
    catchup=False,
)

extract = EmptyOperator(task_id="extract_orders", dag=dag)
fx = EmptyOperator(task_id="refresh_fx", dag=dag)
transform = EmptyOperator(task_id="transform_orders", dag=dag)
load = EmptyOperator(task_id="load_fct_orders", dag=dag)

[extract, fx] >> transform >> load

PLAYBOOK: orders_daily
SLA: Finish by 03:30 UTC

CHECKS BEFORE RESTART
- Is Airflow scheduler healthy? (UI health = green)
- Is warehouse available? (simple SELECT 1)

RESTART STEPS
1) Clear failed task(s) in DAG: transform_orders, then run
2) If data gap remains, backfill date range D-1..D (safe window)
3) Verify row counts match expected range (run validation query)

ESCALATION
- If FX service is down > 15 min, page on-call data engineer

Release: v2.0.0
Date: 2026-01-10
Change: Renamed status values (PAID->CAPTURED), added new value RETURNED
Type: BREAKING (downstream dashboards must update filters)
Approved by: Product Analytics Lead
Issue/Reason: Align with payment gateway lifecycle
Migration: Provide a mapping table dim_status_map for 30 days

Drills and exercises

• [ ] Draft an S2T mapping for a simple customer table with three transformations (trim, upper, coalesce).
• [ ] Write a data dictionary for five fields, including allowed values for one categorical field.
• [ ] Sketch a job flow showing two parallel upstream tasks and one final load.
• [ ] Add three realistic failure modes to your runbook with clear recovery steps.
• [ ] Create a change log entry that includes version, date, rationale, and approval.
• [ ] List five limitations/assumptions for your pipeline (e.g., late-arriving data policy).

Common mistakes and debugging tips

• Missing grain/keys in S2T. Tip: Always declare target grain and dedup rules to prevent duplicates.
• Vague field definitions. Tip: Include business meaning and type; add examples and allowed values.
• Outdated diagrams. Tip: Regenerate after each release; link diagram version to code version.
• Runbook lacks context. Tip: Add SLAs, pre-checks, and exact commands/UI paths to act quickly.
• No audit trail for changes. Tip: Maintain a versioned CHANGELOG plus Git tags; note approvals.
• Hidden assumptions. Tip: Document late data handling, timezone, and null-handling rules explicitly.

Mini project: Production-ready handover pack

Create a complete documentation and handover pack for a new daily pipeline that loads web sessions into a warehouse table.

1. S2T mapping: session_id grain, user and device lookups, UTC timestamp normalization, and bot filtering.
2. Data dictionary: 10 fields with business definitions.
3. Job flow: parallel extraction of sessions and device map; transform; load; daily 02:00 UTC.
4. Runbook: SLA 03:15 UTC, restart steps, backfill guidance, escalation.
5. Change log: initial v1.0.0 release entry.
6. Onboarding notes: where to see alerts, how to mute noisy jobs, contacts.
7. Limitations: bots heuristic may misclassify 2–3%; late data window 48 hours.
8. Sign-off: list acceptance tests and capture approvals.

Subskills

• Source To Target Documentation: Clear mapping of source fields to target fields, keys, grain, filters, and transformations.
• Data Dictionary And Field Definitions: Business meaning, technical types, nullability, and allowed values per field.
• Job Flow Diagrams: Visualize dependencies, schedules, retries, and alert points.
• Operational Runbooks: Start/stop/retry steps, validation checks, SLAs, and escalation paths.
• Change Logs And Versioning: Track what changed, why, when, by whom, and the release version.
• Onboarding Notes For Support Teams: Monitoring dashboards, common issues, contacts, and quick starts.
• Known Limitations And Assumptions: Constraints, data gaps, quality caveats, and scope boundaries.
• Stakeholder Sign Off Process: Acceptance criteria, UAT results, and documented approvals.

Next steps

• Pick one real pipeline and produce the full handover pack using the templates above.
• Ask a teammate to follow your runbook without help—iterate based on their feedback.
• Version and tag your docs alongside code. Update them on every release.