Parquet

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Apache Parquet is an open-source columnar storage format widely used in the Hadoop ecosystem and beyond.

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Apache Parquet is a free, open-source columnar storage format for efficient data compression and encoding, used primarily in big data analytics.

Apache Parquet is an open-source columnar storage format widely used in the Hadoop ecosystem and beyond. It was originally developed by Twitter and Cloudera in 2012 and later donated to the Apache Software Foundation. The project is now governed by the Apache Software Foundation and maintained by a community of contributors.

Parquet's design optimizes for both storage efficiency and query performance, making it a standard for data lakes and analytics workloads. The project's Java implementation, parquet-java, is hosted on GitHub under the Apache organization and has over 3,100 stars and 1,500 forks as of June 2026. Parquet is not a company with revenue, funding, or employees; it is a community-driven open-source project.

There are no known controversies, leadership changes, or layoffs associated with the project itself. The format is supported by major data processing engines including Apache Spark, Apache Hive, Apache Impala, and many cloud data warehouses. Its adoption continues to grow as organizations seek efficient storage for large-scale analytics.

The project's most recent commit as of June 2026 was a dependency bump for Jackson, indicating ongoing maintenance. No financial data, funding rounds, or acquisitions are applicable to Parquet as it is not a commercial entity.

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Who buys this

  • Data engineers building data lakes on Hadoop or cloud object storage
  • Analytics teams using Apache Spark, Hive, or Impala for large-scale querying
  • Cloud data warehouse providers (e.g., Amazon Redshift Spectrum, Google BigQuery) that support Parquet as a native format
  • Organizations migrating from row-based formats (CSV, JSON) to columnar storage for cost and performance gains
  • Machine learning pipelines that need efficient storage for feature engineering and model training data

Strengths and what to watch

Strengths

  • Widely adopted open standard with broad engine support (Spark, Hive, Impala, Presto, etc.)
  • Efficient columnar compression and encoding reduces storage costs and improves query performance
  • Active community with regular maintenance and dependency updates (e.g., Jackson 2.21.4, Thrift 0.23.0 as of June 2026)

Watch for

  • As an open-source project, Parquet has no commercial support or SLA; users rely on community or third-party vendors
  • The format's evolution depends on volunteer contributors; major feature development may lag behind proprietary alternatives
  • Competition from newer columnar formats like Apache Arrow and Lance may fragment the ecosystem

Recent moves

Key Information

Industry
Formats
Founded
2012

Frequently Asked Questions

What is Apache Parquet and what is it used for?

Apache Parquet is a free, open-source columnar storage format designed for efficient data compression and encoding. It is primarily used in big data analytics to reduce storage costs and improve query performance in data lakes and analytics workloads.

How does Parquet's columnar format improve performance?

Parquet stores data column by column rather than row by row, enabling better compression and faster queries. This columnar layout reduces I/O by reading only relevant columns, which speeds up analytics on large datasets in engines like Apache Spark and Hive.

Which data processing engines support Apache Parquet?

Parquet is supported by major engines including Apache Spark, Apache Hive, Apache Impala, Presto, and cloud data warehouses like Amazon Redshift Spectrum and Google BigQuery. This broad support makes it a standard format for data lakes and analytics.

Who created Apache Parquet and who maintains it?

Parquet was originally developed by Twitter and Cloudera in 2012 and later donated to the Apache Software Foundation. It is now governed by the Apache Software Foundation and maintained by a community of contributors, with no commercial entity behind it.

What are the advantages of using Parquet over CSV or JSON?

Parquet offers better compression and faster query performance than row-based formats like CSV or JSON. Its columnar storage reduces file sizes and allows engines to read only needed columns, cutting I/O and storage costs for large-scale analytics.

Is Apache Parquet actively maintained and updated?

Yes, Parquet is actively maintained by its community. Recent commits as of June 2026 include dependency bumps for Jackson and Apache Thrift, along with updates to GitHub Actions, indicating ongoing maintenance and support for the project.

Sources

  1. github.com — Project repository, stars, forks, recent commits, and community maintenance activity
  2. github.com — Most recent commit (June 1, 2026) bumping Jackson dependency
  3. github.com — Commit from May 12, 2026 updating GitHub Actions stale bot
  4. github.com — Commit bumping Apache Thrift to 0.23.0, showing ongoing dependency maintenance