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Overview
Apache Arrow is a language-independent columnar memory format and a set of libraries for efficient data interchange and in-memory analytics. Arrow Columnar Format, IPC, Flight, and implementations such as C++, Python, and R let data frames, databases, and analytics engines exchange data without repeatedly converting it into proprietary intermediate layouts.
Features and best fit
Based on official documentation; not hands-on tested · Content checked:
Key features
Exchange data through a common columnar memory layout
Arrow Columnar Format defines a language-independent in-memory representation for numeric, string, nested, and other data types. When multiple systems understand the same layout, they can avoid many format conversions at process boundaries.
Use IPC and Flight across processes and networks
Arrow IPC serializes Arrow data and metadata for interchange between processes. Arrow Flight builds an RPC protocol on the IPC format for services such as storage systems and databases that need to move Arrow data over a network.
Work with the same data model from C++, Python, R, and other languages
This repository contains C++, Python, R, Ruby, and related implementations with column, table, compute, IPC, and Parquet capabilities. Several other implementations, including .NET, Go, Java, JavaScript, and Rust, are maintained in separate repositories.
Sources: [1]
Best fit
Fits ETL, analytics, and database boundaries where conversion cost matters
Arrow is relevant when large datasets move between systems such as Python and C++, data frames and query engines, or databases and analytics services. Longer stretches that can retain a shared columnar representation reduce the need for intermediate conversion.
Before adoption
It does not replace persistent storage formats or databases
Arrow primarily defines in-memory representation and interchange. Persistent file formats such as Parquet and databases serve different roles, and Arrow libraries commonly read and write Parquet as part of a larger pipeline.
Sources: [1]
Implementations and release cycles vary by language
The Arrow project is not delivered as one package version for every language. Go, Java, Rust, and other implementations have separate repositories and releases, so verify the official package and compatibility requirements for the language you use.
Official sources
- [1]Apache Arrow README(2026-10-03)
- [2]Arrow Columnar Format(2026-10-03)
- [3]Apache Arrow installation(2026-10-03)
- [4]Apache Arrow 25.0.1 release(2026-10-03)
- [5]Apache Arrow Apache-2.0 license(2026-10-03)
Supplemental curator note
When investigating analytics performance, measure conversion and serialization costs at system boundaries as well as individual operators. Arrow is a useful comparison point when those boundaries can share a common columnar representation.
Try it in 3 steps
- 1
Install PyArrow 25.0.1
Add the current stable PyArrow package to your Python environment. Activate a virtual environment first if you use one.
python -m pip install pyarrow==25.0.1 - 2
Create a small Arrow Table
Create an in-memory columnar table and inspect its schema and values.
python -c "import pyarrow as pa; t=pa.table({'name':['a','b'],'value':[1,2]}); print(t)" - 3
Write and read an IPC stream
Serialize the table to Arrow IPC and read it back with the same schema.
python -c "import pyarrow as pa; t=pa.table({'x':[1,2,3]}); sink=pa.BufferOutputStream(); w=pa.ipc.new_stream(sink,t.schema); w.write_table(t); w.close(); print(pa.ipc.open_stream(sink.getvalue()).read_all())"
Growth
Growth trends · Last 30 days
17,172 Stars
Trend data is still being collected.
Development activity
Last 90 days · weekly
- Commits (last 30 days)
- 148
- Open PRs
- 382
Development activity is still being collected.
Built with
Categories and tags
GitHub data
GitHub dataView detailed GitHub data
GitHub Topics
- arrow
- parquet
- Stars
- 17,172
- Forks
- 4,339
- Watchers
- 348
- Open issues
- 2,081
- Contributors
- 371
- Owner type
- Organization
- Primary language
- C++
- License
- Apache-2.0
- Repository last updated
- Oct 2, 2026
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