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Describe the stages of a Spark job and strategies to optimize Spark performance for large datasets.

Spark/Big Datahard0.4 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
Swiggy
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
71 wordsInterview-ready
**Why stages matter**: Stages = units of parallelism; boundaries = shuffles. **Stages**: DAG split at shuffle boundaries; each stage = set of tasks (1 per partition). **Optimization**: Reduce shuffles (broadcast); partition well; cache; AQE. For large data: partition pruning; avoid skew; right-size. **Scalability trade-offs**: Fewer stages = less overhead; more shuffles = more cost. **Cost implications**: Shuffle = network + disk; minimize....
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 Swiggy. The answer also includes follow-up discussion points that interviewers commonly explore.

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