Using RL Agent to Detect and Remediate ETL Pipeline Failures
summary
An RL-guided pipeline health agent runs on AWS Lambda + Glue + EventBridge, uses CloudWatch logs and Glue schema metadata, and combines deterministic anomaly rules with interpretable Q-learning and an external safety layer. It detects schema drift, null-rate spikes, type changes and runtime errors and selects bounded actions: retry, schema coercion, rollback, quarantine or escalate. Evaluation on synthetic benchmarks (30 runs) shows a 74.63% success rate with 5.24 minutes average recovery, non‑escalation 88.63% and audit trails for every decision.
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chapters (9)
- 0:00Introduction to RL in ETL
- 2:10System architecture overview
- 3:35RL policy and safety layers
- 7:08Failure classification demo
- 9:20Benchmark performance results
- 10:45RL vs deterministic policies
- 12:29Key engineering takeaways
- 13:13Human oversight and limits
- 14:17Resources and next steps
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