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Describe how you would use PySpark to aggregate and summarize large transaction datasets.

Spark/Big Datamedium0.3 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
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88E|81M|283H
in this category
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1,863
across 7 categories
Asked at these companies
Swiggy
Key Concepts Tested
partitionsparkwindow
Expert AnswerPremium
55 wordsInterview-ready
**Why aggregation strategy matters**: Wrong approach = OOM or 10x runtime. **Approach**: `df.groupBy("category").agg(sum("amount"), count("*"))`. For large: partition by date; filter early; use window functions for running metrics. **Scalability trade-offs**: Shuffle on groupBy; incremental for streams. **Cost implications**: Full scan = expensive; partition pruning reduces....
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