Observability for Data Pipelines: Monitoring, Alerting & Tracing Lineage
summary
Argues that reliable pipeline operations depend on concrete practices: store immutable data, record data lineage by tagging records or using distributed tracers, and provide a dry run mode plus unit and regression tests so transformations are reproducible and debuggable. Recommends measuring concrete metrics—batch duration, stream latency, ingress/egress and age of oldest unprocessed record—and visualizing them with Prometheus and Grafana to alert on SLA breaches and page on symptoms rather than conjecture.
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chapters (11)
- 0:00Intro
- 0:56Data Pipelines
- 3:20What could go wrong?
- 5:16Data pipeline concerns
- 6:14Interpretability
- 7:42Observability
- 8:44Pipeline features
- 9:17Immutable data
- 10:52Data lineage
- 13:08Dry run mode
- 15:21Testing, monitoring & alerting
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