**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Transformations: lazy, build DAG (map, filter, join). Actions: eager, trigger execution (count, collect, write). Transformations return DataFrame/RDD; actions return data or side effects. Example: df.filter() is transformation; df.count() is action....
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 BCG. The answer also includes follow-up discussion points that interviewers commonly explore.
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