itsual Data Pipeline Engineering
Overview
Data pipelines are production systems. They need the same attention to reliability, testing, and observability as any other critical path.
When to Use
- Building or reviewing data ingestion and transformation pipelines
- Moving from ad-hoc scripts to production-grade data flows
- Improving reliability or cost of existing pipelines
Key Concerns
- Idempotency and exactly-once / at-least-once semantics
- Schema evolution and data contracts
- Late / out-of-order data
- Monitoring of freshness, volume, and quality
- Backfill and replay capabilities
- Cost of storage and compute
Principles
- Treat data pipelines as products with SLAs
- Make failures visible and recoverable
- Prefer declarative and testable transformations where possible
Verification
- Pipeline health (freshness, success rate, data quality) is monitored
- Reprocessing or backfill is possible without major heroics
- Schema changes are handled deliberately