**Why systematic optimization matters**: Ad-hoc fixes don't scale; methodical profiling does. **Approach**: (1) Profile—Spark UI: stages, tasks, shuffle read/write. (2) Partition pruning and predicate pushdown. (3) Broadcast small tables. (4) Repartition for skew. (5) Cache reused DataFrames. (6) Tune shuffle partitions. (7) Enable AQE. (8) Right-size executors. **Scalability trade-offs**: Each lever has limits; combine for best effect....
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 EPAM. The answer also includes follow-up discussion points that interviewers commonly explore.
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