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Data Management in Production
Production data pipelines, feature serving, governance, and data quality at scale
Production Data Pipelines
Running and operating data pipelines in production for training and inference.
Requirements
- SLA/SLO for pipeline completion (e.g. “training data refreshed by 6am”)
- Idempotency and exactly-once or at-least-once semantics where needed
- Monitoring: run duration, failure rate, data freshness, row counts
- Alerting on failures, schema changes, or anomalous volumes
- Rollback: ability to re-run from a previous version or revert outputs
Operational Practices
- Version pipeline code and config; deploy via CI/CD like application code
- Log lineage (inputs → outputs) for audits and debugging
- Use the same pipeline code path as dev/staging where possible; differ only by config and scale