When handling CSV, JSON, and Parquet data types for consumption, what advantages do Parquet files typically offer over the others?

Question

Options

• AJSON files offer more flexibility in schema changes
• BCSV files are more efficient in handling nested data structures
• CParquet files provide better compression and query performance
• DParquet files are not suitable for large datasets

• A
  2% (1)
• B
  2% (1)
• C
  92% (48)
• D
  4% (2)

Explanation

Parquet is a columnar storage format designed specifically for analytical workloads, which means it compresses data far more efficiently than row-based formats and allows query engines to scan only the columns needed rather than entire rows - dramatically improving both storage costs and query speed. Option A is backwards: JSON is flexible for schema changes, but that flexibility comes at a performance cost, not an advantage for data consumption at scale. Option B is also inverted - CSV is a flat, row-based format with no native support for nested structures, whereas JSON and Parquet both handle nesting well (Parquet especially so with its nested encoding). Option D is the opposite of reality: Parquet was built for large datasets and is the dominant format in big data ecosystems like Spark, Hive, and Athena precisely because it scales so well.

Memory tip: Think of Parquet as a "column filing cabinet" - instead of pulling out an entire drawer (row) to find one piece of paper (column), you go straight to the right column drawer. That's why it's faster and more compressed for analytical queries.

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