- name
- data-pipelines
- description
- Deep data pipeline workflow—ingestion, orchestration, idempotency, data quality, SLAs, observability, and lineage. Use when building batch/stream pipelines, debugging job failures, or hardening ETL/ELT.
Data Pipelines
Pipelines fail on silent schema drift, partial writes, and unclear ownership. Design for at-least-once delivery, idempotent sinks, and observable stages.
When to Offer This Workflow
Trigger conditions:
- Batch or streaming ingestion (Kafka, Fivetran, Airflow, Dagster, Spark, etc.)
- Late data, backfills, or schema changes breaking jobs
- SLA misses on freshness or row counts
Initial offer:
Use six stages: (1) requirements & SLAs, (2) source contracts, (3) transforms & idempotency, (4) orchestration & dependencies, (5) quality & monitoring, (6) lineage & operations). Confirm batch vs stream and cloud stack.
Stage 1: Requirements & SLAs
Goal: Freshness (latency), completeness expectations, cost ceiling, failure tolerance (quarantine vs stop-the-line).
Exit condition: SLA table: pipeline → metric → threshold.
Stage 2: Source Contracts
Goal: Schema versioning; CDC vs snapshot pulls; API rate limits.
Practices
- Raw landing zone immutable; curated layers downstream
Stage 3: Transforms & Idempotency
Goal: Deterministic transforms; upsert keys; partition strategy for rewinds.
Practices
- Watermark progress for incremental loads
Stage 4: Orchestration & Dependencies
Goal: Clear DAG; retry policy; backfill without double counting; SLA miss alerts.
Stage 5: Quality & Monitoring
Goal: Data quality checks (null spikes, row bounds, referential checks); metrics on lag, duration, error rate.
Stage 6: Lineage & Operations
Goal: Column-level lineage where valuable; on-call runbook; ownership per pipeline.
Final Review Checklist
- [ ] SLAs and failure policy explicit
- [ ] Source contracts and schema evolution path
- [ ] Idempotent writes and checkpointing
- [ ] Orchestration with retries and safe backfill
- [ ] Data quality checks and alerts
- [ ] Lineage and ownership documented
Tips for Effective Guidance
- Separate compute from storage cost awareness for large shuffles.
- Pair with etl-design for batch patterns and message-queues for streaming handoffs.
Handling Deviations
- Single-script pipelines: still document inputs, outputs, and schedule.