Design a cost-aware resource strategy for a Databricks workload with spiky and batch jobs. Explain Dynamic Resource Allocation, when to disable it, and how min/max executors and spot instances affect cost and SLAs.

Section 5 — Pro-Tip
Pro-Move: 'We use 70% spot with 2x parallelism so one preemption doesn't double runtime.' Red Flag: Running 24/7 clusters for batch jobs.

Supplemental (Senior Context): In production, monitor partition skew, consumer lag, and merge duration. Use correlation IDs for traceability across pipeline stages. Schema evolution: prefer additive changes only; use Schema Registry for streaming to enforce compatibility. Consider data contract tests in CI to catch breaking changes early. Budget 10-20% overhead for replication, checkpoint storage, and DLQ. Data quality gates at each layer prevent bad data propagation. Right-size resources: profile before scaling; over-provisioning wastes budget. Document runbooks for common failures: broker restart, consumer rebalance, sink timeout. Establish SLOs per stage: ingest latency, transform duration, serve freshness. Review partition key choice: avoid high-cardinality keys that cause explosion; use composite keys (date, tenant) for balanced distribution. Test failure injection: kill executors, broker, sink to validate recovery. Optimize for the common case: most queries filter by date. Cold start mitigation: pre-warm connections, cache dimension lookups. Alert on lag exceeding 1hr, error rate above 1%. Cost optimization: lifecycle policies, spot instances, partition pruning. Lineage tracking enables impact analysis. Idempotency keys for replay. Backpressure handling prevents slow consumers from blocking producers. Fan-out patterns allow multiple consumers without re-processing. Exactly-once semantics require replayable source and idempotent sink. Data skew mitigation via salting for high-cardinality joins. Partitioning strategies must align with query patterns for pruning. CAP trade-off: AP for ingest and transform; CP for serve when BI needs accuracy. Blast radius bounded by partition and consumer group. Measure and iterate: latency percentiles, cost per record, error rate. Principal engineer tip: quantify before and after optimizations. Red flag: describing architecture without trade-offs. Glue versus EMR: Glue for bursty sub-2hr jobs; EMR for sustained 8hr+ saving 60%. MSK for Kafka; S3 for lake storage. Self-heal: orchestration retries; idempotent sinks ensure consistency. If primary fails, downstream goes stale but no data loss with replay. Design for operability: runbooks, dashboards, alerts. Avoid tight coupling between stages. Incremental processing reduces compute versus full refresh. Watermark-based deduplication enables idempotency. Partition evolution: add new partitions without rewriting. Retention policies balance cost and compliance. Test at scale: use production-size samples for validation. Always document trade-offs.