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What performance optimization techniques have you applied in Spark, Sqoop, or Databricks?

Spark/Big Datahard0.3 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
Capgemini
Key Concepts Tested
optimizationpartitionsparksql
Expert AnswerPremium
57 wordsInterview-ready
**Spark**: Partitioning, broadcast, AQE, salting, cache, predicate pushdown, coalesce, Kryo, executor tuning. **Sqoop**: --num-mappers, --fetch-size, --split-by on indexed column, --direct (MySQL), --compress. **Databricks**: Photon, Delta OPTIMIZE, autoscaling, spot, adaptive concurrency. **Process**: Profile first. Apply incrementally. Document. A/B test in staging. **Scalability Trade-offs**: Each technique context-dependent. Combine. **Cost Implications**: 2–5x speedup typical....
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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