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

Spark/Big Datahard0.5 min readPremium

**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...

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Low

Asked at 1 company

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Disney+ Hotstar. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, 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

105 words

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. Best practices: use for analytics workloads; choose appropriate block size; enable predicate pushdown; leverage nested types when useful.

Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.

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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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