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What are Spark optimizations, and can you explain them?

Spark/Big Datahard0.6 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Cognizant
Key Concepts Tested
joinoptimizationpartitionsparksql
Expert AnswerPremium
110 wordsInterview-ready
**Partitioning**: repartition by key before shuffle; coalesce before write. Reduces skew and small files. **Broadcast Join**: Small table < 100MB to all executors; no shuffle. Critical for star schema. **Predicate Pushdown**: Filters pushed to Parquet/ORC; skip row groups. Partition pruning on directory structure. **Caching**: MEMORY_AND_DISK for reused DFs; unpersist when done. **AQE**: Runtime coalesce of shuffle partitions; skew join split; broadcast join switch....
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 Cognizant. The answer also includes follow-up discussion points that interviewers commonly explore.

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