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Discuss file formats (Parquet, Avro, ORC) and storage strategies.

Spark/Big Datahard0.4 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-24

**Why format choice matters**: Access pattern and tooling drive selection. **Parquet**: Columnar; compression; predicate pushdown; analytics standard. **Avro**: Row-based; schema evolution; good for streaming/CDC. **ORC**: Columnar; Hive; ACID. **Strategy**: Parquet for...

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Asked at 1 company

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452

questions in Spark/Big Data

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88E|81M|283H

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Key Concepts Tested

partition

Expert Answer

74 words

Why format choice matters: Access pattern and tooling drive selection. Parquet: Columnar; compression; predicate pushdown; analytics standard. Avro: Row-based; schema evolution; good for streaming/CDC. ORC: Columnar; Hive; ACID. Strategy: Parquet for analytics; Avro for events/CDC; partition by date. Scalability trade-offs: Columnar = better for analytics; row = better for streaming. Cost implications: Columnar = less I/O for column access; partitioning reduces scan. Best practice: Parquet for lake; Avro for Kafka; partition by query pattern.

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Pro Tip

Red Flag: One format for everything. Pro-Move: 'Parquet for marts; Avro for CDC—format matches access.'

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