**Pipeline:** Throughput, latency, failure rate, SLA attainment. **Infra:** CPU/memory, shuffle spill, GC. **Data quality:** Null rates, schema drift, freshness. **Cost:** Compute hours, storage. **Business:** Records processed, downstream impact. **Tools:** Grafana, Datadog; PagerDuty. **Why:** SLOs + alerts on breach. Business metrics for prioritization....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Moonfare. The answer also includes follow-up discussion points that interviewers commonly explore.
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