A critical Airflow DAG that loads daily data intermittently fails due to a flaky upstream API. Describe the step-by-step approach you would take to make the pipeline more resilient and to reduce manual intervention (retries, backoff, idempotency, monitoring, alerting, and local caching strategies).

First I’d treat the DAG as two responsibilities: data acquisition and data processing. For acquisition I add robust retries with exponential backoff and jitter in the Airflow operator (max_retries + max_retry_delay), and implement a circuit breaker tracked in Redis so we stop hammering a failing API. I’d also write responses to an S3/Redis cache with TTL so the DAG can fall back to last-known-good data when the API is flaky, and publish failures to a Kafka dead-letter topic for later replay. For processing I enforce idempotency (unique request IDs, upserts in Spark/SQL) so re-runs are safe. I’d add SLA callbacks, Prometheus/Grafana metrics, and PagerDuty alerts for >3 failures/hour, and a runbook in Git. These changes cut manual retries from ~6 hours/week to ~30 minutes and raised DAG success from 92% to ~99.6%.