BigData / Apache Parquet Interview Questions
What is Apache Parquet and why is it used?
Apache Parquet is an open-source, columnar storage file format designed for the Hadoop ecosystem and modern big-data platforms. Unlike row-based formats (CSV, JSON), Parquet stores each column's data contiguously on disk. This layout allows query engines to read only the columns they need, dramatically reducing I/O.
Key reasons Parquet is widely used:
- Columnar layout — queries touch only the relevant columns, not full rows.
- Efficient compression — columns hold homogeneous types, so codecs like Snappy and GZIP achieve higher ratios.
- Predicate pushdown — embedded min/max statistics let engines skip entire row groups that cannot satisfy a WHERE clause.
- Schema embedding — the schema is stored inside the file, eliminating external schema management.
- Broad ecosystem support — Spark, Hive, Presto, Flink, DuckDB, Pandas, AWS Athena, and Snowflake all read/write Parquet natively.
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