# apache/arrow

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

Repository: https://github.com/apache/arrow
Canonical: https://ross.abutalabs.com/products/arrow
Homepage: https://arrow.apache.org/
Language: C++
License: Apache-2.0
License Family: permissive
Topics: arrow, parquet
Last push: 2026-08-26T14:01:00+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 85, longevity 100
- inputs: {"age_days": 3850, "days_push": 7, "days_rel": 23, "gap_med": 63, "n_releases_24m": 12}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 17062, forks 4260 (observed 2026-08-28T04:11:17.643220+00:00)

## What it is
Apache Arrow is a language-independent columnar in-memory data format specification plus multi-language libraries (C++, Python/PyArrow, Java, Rust, Go, and more) for fast data interchange and in-memory analytics. It includes IPC serialization, the Flight RPC protocol, and readers/writers for formats like Parquet, CSV, and ORC.

## Use cases
- move tabular data between pandas and a database without serialization overhead
- read and write parquet files fast in python
- share columnar data between processes with zero-copy
- build an analytics engine on a standard in-memory format
- stream large datasets over the network with Arrow Flight
- exchange data between services written in different languages

## When to choose
- you need high-performance columnar data interchange across languages
- you process large tabular datasets and want zero-copy, SIMD-friendly memory layouts
- you need fast Parquet/CSV/ORC IO or Flight RPC for data services

## When to avoid
- you only need simple row-based CRUD with a small dataset
- your data is deeply mutable and update-heavy rather than analytical scans
- you need a full end-user application rather than a data library

## Facets
- artifact type: library
- maturity: stable
- function: serialization, data-science, etl, rpc, database-driver
- domain: big-data, databases, analytics
- platform: cross-platform, cpp, python, jvm, rust, go
- tags: columnar-format, parquet, in-memory-analytics, zero-copy, ipc, data-interchange, data-engineering, gpu

## Member repositories
- apache/arrow (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:17.643220+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:03:30.881740+00:00, confidence not recorded.
  - readme: https://github.com/apache/arrow (fetched 2026-08-28T04:11:17.643220+00:00, sha ceb2a2833eed)
  - homepage: https://arrow.apache.org/ (fetched 2026-08-29T08:01:48.575134+00:00, sha 6d4fe8b813e4)
  - site_page: https://arrow.apache.org/install (fetched 2026-08-29T08:01:48.587648+00:00, sha 49746bd5a01f)
  - site_page: https://arrow.apache.org/docs (fetched 2026-08-29T08:01:48.593788+00:00, sha 019af2cc6d05)
  - site_page: https://arrow.apache.org/docs/format/Columnar.html (fetched 2026-08-29T08:01:48.597326+00:00, sha 1865d8487754)
  - site_page: https://arrow.apache.org/docs/c_glib (fetched 2026-08-29T08:01:48.600968+00:00, sha f10a4eb11b9b)
  - site_page: https://arrow.apache.org/docs/cpp (fetched 2026-08-29T08:01:48.602656+00:00, sha c6752d031e57)
  - site_page: https://arrow.apache.org/docs/python (fetched 2026-08-29T08:01:48.604447+00:00, sha 7bf112b1dd74)
  - site_page: https://arrow.apache.org/faq (fetched 2026-08-29T08:01:48.584650+00:00, sha 23376e75a49d)
  - site_page: https://arrow.apache.org/release (fetched 2026-08-29T08:01:48.590988+00:00, sha 052acd09a846)
- Data as of 2026-08-30T08:39:29.467469+00:00.