etl-runbook

Document ETL pipeline monitoring, failure diagnosis, and recovery procedures.

19|3|Updated Feb 28, 2026

Search Tags:#monitoring #etl #runbook #backfill #data-pipelines #data-quality

Ready to run

npx skills add https://github.com/qa-aman/claude-skills --skill etl-runbook

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: etl-runbook
Source: https://github.com/qa-aman/claude-skills/tree/main/skills/by-role/data-engineer/etl-runbook
Command: npx skills add https://github.com/qa-aman/claude-skills --skill etl-runbook

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

An ETL runbook is the operational contract for a data job: how to monitor it, diagnose when it breaks, and recover data integrity. It helps on-call engineers quickly isolate issues, restore data quality, and reduce mean time to recovery.

Core Features & Use Cases

Define job overviews with ownership, schedule, and SLA to establish expectations.
Specify healthy data criteria (row counts, freshness, nulls, duplicates) and escalation flows.
Provide a step-by-step diagnostic and recovery playbook for common failure modes, including backfill guidance.

Quick Start

Create and store an ETL runbook documenting the job overview, health criteria, failure diagnosis steps, backfill procedures, and escalation contacts so on-call engineers can follow it during incidents.

Frequently Asked Questions about etl-runbook

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create an ETL runbook for data pipeline monitoring and failure recovery?▼

An ETL runbook documents the operational protocol for data pipelines by defining job ownership, healthy data criteria, diagnostic procedures, and recovery steps to reduce mean time to recovery during incidents.

What should be included in a data pipeline runbook to handle on-call incidents?▼

A data pipeline runbook should include a job overview, healthy-state criteria like row counts and freshness, failure diagnosis steps, backfill guidance, and escalation contacts for on-call engineers.

How do I define healthy data criteria for ETL data quality checks?▼

Healthy data criteria for ETL pipelines are defined by specifying expected row counts, data freshness thresholds, null rates, and duplicate detection rules to establish a baseline for operational monitoring.

What is the best way to document backfill procedures for ETL pipelines?▼

The best way to document backfill procedures is to include explicit recovery steps within an ETL runbook, guiding on-call engineers through data restoration processes to maintain data integrity.

Can I use a single runbook for ETL pipelines across various data environments?▼

Yes, an ETL runbook applies to data jobs across various environments by standardizing the operational contract for monitoring, diagnosing, and recovering pipeline failures regardless of the specific infrastructure.