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Apache Parquet

lastmod 2026-03-06created 2025-06-047 edits

Apache Parquet is a column-oriented, open-source data file format designed to support efficient data storage and retrieval. It further improves the ability to process complex data in bulk and provides efficient data compression and encoding schemes. Apache Parquet is designed to provide a common interchange format for both batch and interactive workloads. It is similar to the other columnar storage file formats available in Hadoop (namely RCFile and ORC).

Categories

  • hyparquet - Ultra-lightweight pure JS Parquet parser
  • Carquet - High-performance pure C library for reading and writing Parquet files

Features of Parquet

  • Uses a free, open-source file format.
  • Language-agnostic.
  • Column-based format - files are organized by column rather than by row, which saves storage space and speeds up analytical queries.
  • Used for analytics (OLAP) use cases, especially alongside traditional OLTP databases.
  • Highly efficient data compression and decompression.
  • Supports complex data types and advanced nested data structures.

Pros of Parquet

  • Well suited for storing big data of any kind (structured data tables, images, videos, documents).
  • Saves cloud storage space by using highly efficient column-wise compression and flexible encoding schemes for columns of different data types.
  • Improves data throughput and performance using techniques such as data skipping. As a result, queries that fetch specific column values do not need to read entire rows of data.

Apache Parquet is implemented using the record shredding and assembly algorithm. This accommodates the complex data structures that can be used to store data. Parquet is optimized for working with complex data in bulk and offers efficient data compression and encoding types through a variety of methods. This approach is best especially for queries that need to read specific columns from large tables, because Parquet reads only the required columns, greatly minimizing IO.

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