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What strategies would you use to optimize Spark jobs for both performance and cost on AWS?

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
Total Bank
1,863
across 7 categories
Asked at these companies
Meesho
Key Concepts Tested
partitionspark
Expert AnswerPremium
80 wordsInterview-ready
**Cluster**: Right-size; spot for batch (60–70% savings). Autoscaling (EMR/Databricks). **Data**: Partitioning, predicate pushdown. S3 lifecycle for raw. Consolidate small files. **Compute**: Instance type (compute-optimized for CPU-bound). Graviton for cost. Tune shuffle, storage. **Ops**: Tag resources. Cost allocation. Benchmark instance families. **Why Both**: Performance = lower runtime. Cost = right resources....
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 Meesho. The answer also includes follow-up discussion points that interviewers commonly explore.

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