Design a resilient, observable Python ETL pipeline with idempotency, retries, and structured logging for production workloads.
## CONTEXT I am building or refactoring a batch ETL pipeline in Python that pulls data from one or more sources, transforms it, and lands it in a warehouse or lake. My current setup is fragile: failures are silent, reruns create duplicates, and debugging means reading raw logs. I want a 2026-current, production-ready…
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