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Home/Questions/Spark/Big Data/Discuss file formats (Parquet, Avro, ORC) and storage strategies.

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

Spark/Big Datahard0.4 min readPremium

**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...

🤖 Analyze Your Answer
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
Apple
Key Concepts Tested
partition

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Apple. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition) will help you answer variations of this question confidently.

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

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.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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