**Situation**: Legacy ETL (Informatica, SSIS) couldn't scale to TB; per-row licensing expensive.
**Task**: Build scalable, cost-effective data platform.
**Action**: Chose Spark for (1) **Scale**—handles TB. (2) **Cost**—open source. (3) **Flexibility**—code-based, custom logic. (4) **Unified**—batch + streaming. (5) **Ecosystem**—Delta, MLlib, connectors.
**Result**: 10x data volume, 60% cost reduction. ETL tools for simple, operational pipelines.
**Trade-offs**: Spark = more engineering....
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 Tiger Analytics. The answer also includes follow-up discussion points that interviewers commonly explore.
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