**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Dataset transformation: Define input/output schema, transformations (filter, join, agg, pivot), write PySpark. Example: Normalize, deduplicate, aggregate. Best practice: Document logic; handle nulls; validate output.
**Scalability trade-offs**: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling....
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 Capgemini. The answer also includes follow-up discussion points that interviewers commonly explore.
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