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What performance tuning techniques do you apply in both Sqoop and Spark to optimize their execution?

Spark/Big Datamedium0.4 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
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1,863
across 7 categories
Asked at these companies
Infosys
Key Concepts Tested
joinpartitionsparksql
Expert AnswerPremium
79 wordsInterview-ready
**Sqoop**: --num-mappers 4–8 (match source capacity). --fetch-size 10000. --direct for MySQL. --compress. --split-by on indexed column. **Spark**: Repartition, broadcast joins, cache, AQE, coalesce, predicate pushdown, executor sizing. **Shared**: Off-peak runs. Tune parallelism to source limits. Columnar formats (Parquet) for downstream. **Why Both**: Sqoop = source-side tuning. Spark = processing-side....
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 Infosys. The answer also includes follow-up discussion points that interviewers commonly explore.

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