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Explain the benefits of using columnar storage formats like Parquet or ORC.

Spark/Big Datahard0.5 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
Disney+ Hotstar
Key Concepts Tested
optimizationpartition
Expert AnswerPremium
105 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Columnar formats (Parquet, ORC): store by column not row. Benefits: (1) Compression—similar data compresses well; (2) Predicate pushdown—read only needed columns; (3) Better for analytics—aggregations, scans; (4) Schema embedded. Parquet is widely supported; ORC has better compression in Hive. Example: SELECT date, sum(amount) reads only those columns....
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 Disney+ Hotstar. The answer also includes follow-up discussion points that interviewers commonly explore.

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