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Discuss common transformations used in Spark code.

Spark/Big Datahard0.3 min readPremium
Frequency
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Asked at 1 company
Category
452
questions in Spark/Big Data
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88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Datametica
Key Concepts Tested
joinoptimizationsparkwindow
Expert AnswerPremium
59 wordsInterview-ready
**Why transformations matter**: Choice affects Catalyst optimization and performance. **Common**: map, filter, select, withColumn, drop, groupBy, agg, join, union, distinct. Window: row_number, rank, sum over. **Scalability trade-offs**: Narrow (map, filter) = no shuffle; wide (groupBy, join) = shuffle. Prefer narrow; minimize wide. **Cost implications**: UDF breaks optimization; built-ins preferred....
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