Parquet GitHub Repositories (2026) · Open Source Tools

High-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale

Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics

A fast minimal dependency implementation of Apache Parquet

Embedded retrieval library built on Parquet. Fast, efficient, and scalable.

Parseable is an open source, unified infrastructure observability platform built in Rust on a data lake architecture. It tracks logs, metrics, traces, and events across apps, agents, and systems, reducing storage costs by up to 90% through columnar telemetry compression.

The fastest business intelligence tool for humans and agents.

Apache Kafka® compatible broker with S3, PostgreSQL, SQLite, Apache Iceberg and Delta Lake

Stream Postgres to Apache Iceberg on S3 via logical replication, queryable over the Postgres wire protocol.

OLake - Fastest Databases, Kafka & S3 Replication to Apache Iceberg with Table optimization (Called OLake Fusion). ⚡ Efficient, quick and scalable data ingestion for real-time analytics. Supported sources : Postgres, MongoDB, MySQL, Oracle, MSSql, DB2, Kafka, S3.

ParquetSharp is a .NET library for reading and writing Apache Parquet files.

Official Rust implementation of Apache Arrow

Apache Drill is a distributed MPP query layer for self describing data

Drop-in Apache Spark replacement written in Rust, unifying batch processing, stream processing, and compute-intensive AI workloads.

Apache Parquet Java

A command line tool to query an ODBC data source and write the result into a parquet file.

parquet file parser for javascript

Pushdown cache for DataFusion

A minimal Python library for Apache Arrow, connecting to the Rust Arrow crate

Quilt is a Scientific Data Management Platform on AWS that helps teams and AI find, trust, and reuse data through deeply versioned, context-rich data packages.

Datos públicos de Chile curados, normalizados y validados: geografía, demografía, economía, salud, educación y distritos electorales. Listos para consumir en una línea de código con Polars, DuckDB, SQLite y Excel.