README.md

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# Apache Arrow

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## Powering In-Memory Analytics

Apache Arrow is a development platform for in-memory analytics. It contains a
set of technologies that enable big data systems to process and move data fast.

Major components of the project include:

 - [The Arrow Columnar In-Memory Format](https://arrow.apache.org/docs/dev/format/Columnar.html):
   a standard and efficient in-memory representation of various datatypes, plain or nested
 - [The Arrow IPC Format](https://arrow.apache.org/docs/dev/format/Columnar.html#serialization-and-interprocess-communication-ipc):
   an efficient serialization of the Arrow format and associated metadata,
   for communication between processes and heterogeneous environments
 - [The Arrow Flight RPC protocol](https://github.com/apache/arrow/tree/main/format/Flight.proto):
   based on the Arrow IPC format, a building block for remote services exchanging
   Arrow data with application-defined semantics (for example a storage server or a database)
 - [C++ libraries](https://github.com/apache/arrow/tree/main/cpp)
 - [C bindings using GLib](https://github.com/apache/arrow/tree/main/c_glib)
 - [C# .NET libraries](https://github.com/apache/arrow/tree/main/csharp)
 - [Gandiva](https://github.com/apache/arrow/tree/main/cpp/src/gandiva):
   an [LLVM](https://llvm.org)-based Arrow expression compiler, part of the C++ codebase
 - [Go libraries](https://github.com/apache/arrow/tree/main/go)
 - [Java libraries](https://github.com/apache/arrow/tree/main/java)
 - [JavaScript libraries](https://github.com/apache/arrow/tree/main/js)
 - [Python libraries](https://github.com/apache/arrow/tree/main/python)
 - [R libraries](https://github.com/apache/arrow/tree/main/r)
 - [Ruby libraries](https://github.com/apache/arrow/tree/main/ruby)
 - [Rust libraries](https://github.com/apache/arrow-rs)

Arrow is an [Apache Software Foundation](https://www.apache.org) project. Learn more at
[arrow.apache.org](https://arrow.apache.org).

## What's in the Arrow libraries?

The reference Arrow libraries contain many distinct software components:

- Columnar vector and table-like containers (similar to data frames) supporting
  flat or nested types
- Fast, language agnostic metadata messaging layer (using Google's Flatbuffers
  library)
- Reference-counted off-heap buffer memory management, for zero-copy memory
  sharing and handling memory-mapped files
- IO interfaces to local and remote filesystems
- Self-describing binary wire formats (streaming and batch/file-like) for
  remote procedure calls (RPC) and interprocess communication (IPC)
- Integration tests for verifying binary compatibility between the
  implementations (e.g. sending data from Java to C++)
- Conversions to and from other in-memory data structures
- Readers and writers for various widely-used file formats (such as Parquet, CSV)

## Implementation status

The official Arrow libraries in this repository are in different stages of
implementing the Arrow format and related features.  See our current
[feature matrix](https://arrow.apache.org/docs/dev/status.html)
on git main.

## How to Contribute

Please read our latest [project contribution guide][5].

## Getting involved

Even if you do not plan to contribute to Apache Arrow itself or Arrow
integrations in other projects, we'd be happy to have you involved:

- Join the mailing list: send an email to
  [dev-subscribe@arrow.apache.org][1]. Share your ideas and use cases for the
  project.
- Follow our activity on [GitHub issues][3]
- [Learn the format][2]
- Contribute code to one of the reference implementations

[1]: mailto:dev-subscribe@arrow.apache.org
[2]: https://github.com/apache/arrow/tree/main/format
[3]: https://github.com/apache/arrow/issues
[4]: https://github.com/apache/arrow
[5]: https://arrow.apache.org/docs/dev/developers/index.html