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Explain Spark transformations (lazy evaluation, wide vs narrow).

Spark/Big Datahard0.6 min readPremium
Frequency
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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
Apple
Key Concepts Tested
joinoptimizationpartitionspark
Expert AnswerPremium
121 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Spark transformations are lazy—they build a DAG and execute only when an action triggers. Narrow transformations (map, filter) don't require data movement; each partition is processed independently. Wide transformations (groupBy, join, distinct) require shuffle—data is redistributed across partitions. Narrow: df.filter(col('x')>0)....
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 Apple. The answer also includes follow-up discussion points that interviewers commonly explore.

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