Data Pipeline Operational Runbook Template

Purpose & Scope

This runbook documents how to operate, monitor, triage, and recover a data pipeline. Use it to reduce downtime, speed mean-time-to-resolution, and keep stakeholders informed. Adapt the examples, command templates, and checklists to your stack (orchestration, messaging, storage, transformations, observability tools).

Pipeline Overview & Owners

Brief architecture summary (components, data sources, destinations, and critical SLAs):

• Pipeline name: [name]
• Purpose: [short description of business value]
• Data sources: [source A, source B]
• Destinations: [warehouse, lake, downstream service]
• Critical components: orchestration, ingestion connectors, message broker, transformation engine, data storage, metadata/catalog
• Primary owner: [team or person, contact methods]
• Secondary owner/on-call: [team or person, contact methods]

Contacts & Escalation Matrix

1. Operator / On-call (first responder) — [name/email/phone/slack]
2. Pipeline engineer / owner — [name/email/phone]
3. Platform / SRE — [name/email/pager]
4. Data product owner / business contact — [name/email]
5. Compliance / Security (if data sensitivity issue) — [name/contact]

Expected SLAs and SLOs

Record measurable targets and acceptable error budgets. Example table:

• End-to-end latency: 95% of records processed within 15 minutes
• Daily freshness: Data available by 04:00 UTC (SLA)
• Data completeness: Missing rows < 0.1% per upstream file
• Availability: 99.9% for ingestion components

Daily Health Checks & Dashboards

Minimal daily checks for on-call or owner to run or glance at dashboards.

• Check orchestration UI for failed tasks in the last 24 hours
• Verify consumer lag for streaming topics (keep within threshold)
• Confirm daily ingestion volumes vs expected (spike/drop)
• Check error-rate and retry queues (dead-letter topics)
• Confirm downstream table row counts and recent refresh timestamps
• Storage and cost alarms (e.g., runaway partitions or retention problems)
• Data quality quick checks (null rates, schema drift indicators)

Key Dashboards & Metrics

• Orchestration: job success rate, run durations, failure trends
• Messages: consumer lag, throughput, publish errors
• Transformations: task-level error counts, execution times
• Storage: table sizes, partition counts, retention anomalies
• Data Quality: row-level validation failures, null-rate trends

Common Alerts and Classification

Classify alerts to guide urgency and response.

• Severity 1 (P1): Pipeline down, customers affected, SLA breach imminent — immediate response and page the on-call.
• Severity 2 (P2): Partial failure, degraded performance, missed non-critical refresh — investigate within 1 hour.
• Severity 3 (P3): Non-urgent warnings, minor data quality issues, operational housekeeping — address in sprint.

Triage Playbook — step-by-step

Use this flow for initial triage. Replace generic commands with your environment's tools (Airflow/GCP Dataflow/Kubernetes/kafka-console/psql/dbt CLI, etc.).

1. Gather context:
   • When did the issue start? Which runs/jobs/tasks show failures?
   • Which downstream consumers are impacted?
   • Are there related alerts across platform components?
2. Check orchestration:
   • Example: check latest DAG/job status — replace with your command/UI
   • Command template: <orchestration-cli> status <job-id>
3. Check ingestion connectors:
   • Confirm connector health, backpressure, or authentication errors
   • Command template: <connector-cli> describe <connector-name>
4. Check messaging layer:
   • Consumer lag and partition offsets
   • Command template: <kafka-cli> consumer-lag --topic <topic> --group <group>
5. Examine logs and error messages:
   • Search for recent errors across service logs and trace IDs
6. Quick remediation attempts (if safe):
   • Restart failed task / restart connector process
   • Rerun failed job for the affected date range (use isolation and dry-run when possible)
7. Escalate if unresolved: follow the Escalation Matrix and page SRE or platform team for infrastructure-related issues.
8. Communicate: post an incident update using the template below to stakeholders.

Rollback & Remediation Steps

When a remediation must change production state, follow safe rollback practices and approvals.

• Safe rollback checklist:
  1. Identify scope: which downstream tables/partitions/streams are affected?
  2. Verify backup availability (snapshots, previous export files)
  3. Confirm a rollback window and notify stakeholders
  4. Execute rollback in a staging environment first where feasible
  5. Apply rollback and validate with targeted tests (row counts, checksums, Q/A queries)
  6. Monitor for regression after rollback
• Schema change rollback: do not automatically revert schema migrations — coordinate with consumers, materialized views, and consumers that may require transformation changes.
• Data correction: use idempotent upserts, backfill jobs, or targeted transformation fixes rather than broad deletes where possible.

Incident Communication Templates

Use short, consistent messages during incidents.

Status update (initial): "We are investigating an issue affecting [pipeline name]. Customers may see delayed or missing data. Current estimate to next update: 30 minutes. On-call: [name]."

Post-restoration update: "Processing has resumed for [pipeline name]. We are validating data integrity and will follow up with a post-incident review. Impact window: [start] to [end]."

Post-Incident Review Template

Capture facts and actions to reduce recurrence.

1. Incident title and ID
2. Date/time detected, duration, severity
3. What happened (concise timeline)
4. Root cause analysis (evidence-based; include logs and traces)
5. Immediate mitigations and permanent fixes
6. Action owners, due dates, and verification plan
7. Lessons learned and updates to runbook or monitoring

Onboarding Checklist for New Pipelines

• Document owners and contact info
• Define SLAs/SLOs and register them with SLO monitoring
• Provision dashboards and alerts with defined thresholds
• Create runbook entry with run commands and rollback steps
• Run chaos / failure scenarios in staging and document outcomes
• Set up data quality checks and instrumentation
• Confirm compliance and data sensitivity handling

Example Entries

Batch job example

• Job: nightly_orders_etl
• Schedule: daily 02:00 UTC
• Expected run time: < 30m
• Command template: <orchestration-cli> trigger nightly_orders_etl --run-date {{date}}
• Health checks: success count, failure rate, output row counts, timestamp of last successful run

Streaming connector example

• Connector: payments-to-kafka
• Expected throughput: 1000 msg/s
• Key metrics: consumer lag < 1 minute, error-rate < 0.1%
• Commands: <connector-cli> status payments-to-kafka; <kafka-cli> consumer-lag --topic payments
• Remediation: restart connector, check credentials, verify source availability

Runbook Maintenance & Versioning

Store this runbook near the pipeline code and in the knowledge base. Record changes with a version number, change summary, and author. Review and test runbook steps quarterly or after major changes.

Where to Customize

Replace generic command templates with your environment-specific commands (Airflow/Prefect/DBT/Kubernetes/GCP/AWS/Azure CLIs). Add direct links to dashboards, logs, run links, and ticketing/incident systems in the owner and dashboard sections.