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Apache Arrow
Apache Arrow
Apache Arrow
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Apache Arrow
DeveloperApache Software Foundation
Initial releaseOctober 10, 2016; 9 years ago (2016-10-10)
Stable release
22.0.0[1] Edit this on Wikidata / 24 October 2025; 3 months ago (24 October 2025)
Written inC, C++, C#, Go, Java, JavaScript, MATLAB, Python, R, Ruby, Rust
TypeData format, algorithms
LicenseApache License 2.0
Websitearrow.apache.org
Repositorygithub.com/apache/arrow

Apache Arrow is a language-agnostic software framework for developing data analytics applications that process columnar data. It contains a standardized column-oriented memory format that is able to represent flat and hierarchical data for efficient analytic operations on modern CPU and GPU hardware.[2][3][4][5][6] This reduces or eliminates factors that limit the feasibility of working with large sets of data, such as the cost, volatility, or physical constraints of dynamic random-access memory.[7]

Interoperability

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Arrow can be used with Apache Parquet, Apache Spark, NumPy, PySpark, pandas and other data processing libraries. The project includes native software libraries written in C, C++, C#, Go, Java, JavaScript, Julia, MATLAB, Python (PyArrow[8]), R, Ruby, and Rust. Arrow allows for zero-copy reads and fast data access and interchange without serialization overhead between these languages and systems.[2]

Applications

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Arrow has been used in diverse domains, including analytics,[9] genomics,[10][7] and cloud computing.[11]

Comparison to Apache Parquet and ORC

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Apache Parquet and Apache ORC are popular examples of on-disk columnar data formats. Arrow is designed as a complement to these formats for processing data in-memory.[12] The hardware resource engineering trade-offs for in-memory processing vary from those associated with on-disk storage.[13] The Arrow and Parquet projects include libraries that allow for reading and writing data between the two formats.[14]

Governance

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Apache Arrow was announced by The Apache Software Foundation on February 17, 2016,[15] with development led by a coalition of developers from other open source data analytics projects.[16][17][6][18][19] The initial codebase and Java library was seeded by code from Apache Drill.[15]

References

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[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Apache Arrow

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Apache Arrow is a universal columnar format and multi-language toolbox designed for fast data interchange and in-memory analytics, providing a standardized, language-independent representation of structured, table-like datasets in . It defines a columnar layout for flat and nested , optimized for efficient analytic operations on modern hardware through memory locality, vectorization, and support for SIMD instructions. This format reduces the overhead of serialization and deserialization when moving between systems or programming languages, enabling high-performance applications in processing. The project originated from the need to establish standards for tabular data representation and interchange across diverse ecosystems, addressing inefficiencies in data movement and algorithm reuse. Development began in 2015 through collaborations involving developers from projects like , , and Spark, with initial design discussions at events such as Strata NYC. was accepted as a top-level Apache project on February 17, 2016, under the leadership of contributors including of Investments and teams from , MapR, and others. Since its inception, it has evolved from a simple in-memory and (IPC) format to include file formats like and Parquet integration, as well as query processing capabilities. Apache Arrow supports libraries in 13 programming languages, including C++, Python, , , , and , allowing developers to build applications that process and transport large datasets efficiently. It integrates deeply with major open-source projects such as for vectorized user-defined functions, for Parquet I/O in Python, Dask for , and for columnar storage. Additional ecosystem tools include GPU-accelerated analytics via the GPU Open Analytics Initiative and machine learning frameworks like Ray and Hugging Face Datasets, making it a foundational technology for modern data analytics stacks. As of October 2025, the project continues active development, with version 22.0.0 introducing enhancements for performance and compatibility.

Overview

History and Development

Apache Arrow's development originated in 2015 at Dremio, where co-founder Jacques Nadeau initiated the project as an extension of earlier columnar data processing efforts, including those in the Apache Kudu project, which focused on efficient storage and analytics for fast data. Nadeau, serving as the initial Vice President and committer for Arrow, collaborated with a coalition of developers from organizations such as , , and the RISELab at UC Berkeley to standardize an in-memory columnar format. This work built on prior open-source initiatives like and drew inspiration from research on columnar storage systems, aiming to address inefficiencies in data interchange across tools. The project was uniquely accepted as a top-level initiative on February 17, 2016, bypassing the typical incubation phase due to its foundation in established technologies. Key early milestones included the release of 0.1.0 on October 10, 2016, which established the core columnar format specification and initial language bindings for C++, , and Python. Founding contributors like advanced the Python and C++ implementations, while integrations began accelerating adoption; for instance, incorporated in 2017 through PySpark enhancements led by contributors from and , enabling up to 53x faster data transfer between JVM and Python processes. Similarly, integration, driven by McKinney, saw significant progress by 2018, improving interoperability and performance for Python-based . These efforts highlighted Arrow's role in bridging disparate systems without overhead. Subsequent major releases marked Arrow's maturation: version 1.0.0 arrived on July 24, 2020, delivering a stable after four years of iterative development and over 810 resolved issues in the preceding cycle. 2.0.0 followed on October 22, 2020, introducing refinements to the Arrow Flight RPC protocol for high-performance over networks, building on its initial proposal in 2018. Over time, evolved from a mere format specification into a comprehensive platform, incorporating compute kernels through initiatives like (open-sourced by Dremio in 2018) for hardware-accelerated expression evaluation, alongside expanded language support and ecosystem integrations. By 2025, the project had released versions up to 22.0.0, reflecting ongoing community contributions from hundreds of developers.

Goals and Design Principles

Apache Arrow's primary goals center on enabling reads and writes across heterogeneous systems, which allows to be shared without unnecessary copying or reformatting, thereby minimizing latency and usage in data pipelines. It standardizes in-memory interchange to provide a common representation for tabular , facilitating efficient communication between diverse processing environments and reducing the costs associated with and deserialization. Furthermore, Arrow supports analytical workloads by leveraging a columnar format that optimizes for high-throughput operations on large datasets with minimal overhead. The project's design principles prioritize language independence through a universal columnar memory format that defines a shared specification implementable in multiple programming languages, such as C++, Python, and . This format accommodates both flat and nested data structures, including lists, structs, and unions, to handle complex, hierarchical data while maintaining simplicity for basic tabular forms. Optimization for vectorized processing is integral, with contiguous column layouts aligned to 64-byte boundaries to enable SIMD instructions and cache-efficient access on modern hardware like CPUs and GPUs. Extensibility is another key principle, allowing custom data types via metadata extensions without disrupting the core specification. Interoperability serves as a foundational principle, achieved by defining a platform-neutral specification that promotes data exchange across tools and avoids vendor lock-in in big data ecosystems. Performance motivations underscore the need to eliminate data copying in multi-stage pipelines—such as from storage systems to compute engines—enabling high-throughput analytics by supporting direct memory sharing and efficient IPC protocols.

Data Model and Format

Columnar Storage Format

The Apache Arrow columnar storage format defines a standardized, language-agnostic specification for serializing structured data in a columnar layout, enabling efficient on-disk storage and inter-process communication. Central to this is the Inter-Process Communication (IPC) format, which serializes record batches—self-contained units of columnar data—using FlatBuffers for metadata to ensure zero-copy deserialization where possible. The IPC message structure consists of a 32-bit continuation indicator (0xFFFFFFFF), a 32-bit metadata size, the FlatBuffers-encoded metadata, padding to an 8-byte boundary, and the message body comprising one or more buffers that represent the columnar data. Schema metadata in the IPC format includes field names, data types, nullability flags, and details for dictionary encoding, such as dictionary IDs for categorical . Buffer layout follows a flattened pre-order depth-first traversal of the schema's fields, with each column's stored in contiguous buffers: for primitive types like int32 or , this includes a validity for nulls followed by the buffer; for variable-length types like , it adds offset buffers. Complex types, such as lists (with child and offset arrays), structs (aggregating child columns with a shared validity ), and unions (sparse or dense, with type IDs and child buffers), are supported through nested buffer arrangements that preserve the columnar organization. encoding for categorical replaces values with integer indices into a separate buffer, allowing compact representation of repeated strings or enums, with the itself serialized in dedicated DictionaryBatch messages. The Arrow File Format builds on the IPC streaming format by adding structure for random access, beginning with a 6-byte magic prefix ("ARROW1"), followed by the body of zero or more record batches, and ending with a footer containing the , block offsets and sizes, and another magic suffix. This format, often using the ".arrow" file extension (also known as Feather V2), facilitates persistent storage of finite datasets. In contrast, the streaming format supports continuous data transfer via an unbounded sequence of IPC messages—starting with a message, interspersed with DictionaryBatch and RecordBatch messages, and optionally terminated by an end-of-stream marker—suitable for real-time pipelines and using the ".arrows" extension. Encoding mechanisms optimize storage for common patterns: (RLE) is applied in the Run-End Encoded layout for sparse or repetitive data, using a run-ends array of signed integers (16-64 bits) paired with a values array; for nulls and dictionary indices, RLE compresses sequences of identical values. Bit-packing is used for dense primitive types like booleans, where bits are packed into bytes with length rounding to the nearest byte. These encodings ensure compact serialization while maintaining compatibility with the in-memory columnar layout. Schema evolution in Arrow emphasizes interoperability across versions, with rules for allowing readers of newer formats to process older data by ignoring added optional fields or unknown dictionaries, while requiring existing fields to remain unchanged in type and position. enables older readers to handle newer data by treating added nullable fields as absent and skipping unrecognized custom metadata under the reserved "" namespace. These rules, governed by the format's versioning process, support incremental updates without breaking existing implementations.

In-Memory Representation

Apache Arrow's in-memory representation adopts a columnar layout, organizing data into contiguous arrays optimized for analytical workloads. Each array is composed of one or more fixed-size memory buffers that store the data in a language-agnostic manner. For primitive types, such as integers or floats, the layout includes a value buffer containing the actual data values and an optional null bitmap buffer to track nullability, where each bit represents the validity of a corresponding element. This structure ensures high memory locality, enabling efficient sequential access and reducing cache misses during operations like filtering or aggregation. For variable-length data types, including binary, , or list arrays, an additional offset buffer is incorporated to define the start and end positions of each element's data within the value buffer. This design supports nested and complex types by recursively applying the same buffer principles to child arrays, while maintaining overall column contiguity. To accommodate datasets exceeding available RAM, Arrow employs chunked arrays, which partition large columns into multiple smaller arrays (chunks) that can be processed independently or streamed. These chunks share metadata such as and null count, allowing seamless without data duplication or full materialization in memory. A key optimization is , which permits direct access to the underlying buffers without deserialization or data copying. Buffers are designed to be relocatable—meaning their pointers can be shared across libraries or processes via mechanisms like the Arrow C data interface—facilitating efficient slicing, projection, and for in-memory . This avoids the overhead of traditional formats and enables true usage. The representation further supports vectorized processing by aligning buffers to 64-byte boundaries and incorporating padding for uniform vector sizes, which aligns with modern CPU architectures. This layout enables (SIMD) instructions to operate on entire columns in batches, accelerating computations like scans or reductions. Buffers are sized to fit within L1/L2 caches where possible, enhancing performance for large-scale . Memory management relies on for buffers, where each buffer tracks active references to determine when deallocation is safe, preventing memory leaks in multi-threaded or multi-library environments. In implementations for garbage-collected languages like Python, Arrow's buffer references integrate with the host runtime's collector, ensuring automatic cleanup while preserving efficiency across operations.

Implementations and APIs

Language Bindings

Apache Arrow provides official language bindings that implement the columnar format and enable efficient in-memory across multiple programming languages. These bindings are built on the core specification and ensure compatibility with the IPC () format for data exchange. The foundational implementation is the C++ library, which serves as the reference for all other bindings. It offers low-level buffer management through memory pools and buffers that support slicing, allocation, and sharing, allowing efficient handling of large datasets without unnecessary copies. The C++ library also includes a comprehensive set of compute functions, such as aggregation, filtering, sorting, and arithmetic operations, implemented via kernels that operate directly on Arrow arrays and tables. The Python binding, known as PyArrow, builds directly on the C++ core and provides seamless integration with popular libraries. It supports conversion between Arrow arrays and arrays or DataFrames, enabling operations for faster data interchange in analytical workflows. PyArrow includes the Dataset , which facilitates for querying large, partitioned datasets across filesystems without loading everything into memory. The Java binding ensures JVM compatibility by mapping Arrow's columnar vectors to Java objects, supporting most primitive and nested data types. It provides readers and writers for IPC streams and files, along with compression support, and integrates with Apache Spark by allowing Arrow vectors to be used within Spark DataFrames for optimized data processing in distributed environments. Other official bindings include JavaScript for Node.js and browser environments, Go, Rust, C#, and Ruby, each offering native type mappings to Arrow's data structures for platform-specific applications. The JavaScript binding supports IPC streaming and file I/O for web-based data visualization and processing. Go's implementation emphasizes efficient data transfer with full support for compression and Flight RPC. Rust provides high-performance compute kernels and IPC handling, leveraging the language's memory safety. C# enables .NET integration for enterprise data pipelines, while Ruby focuses on basic array construction and IPC for scripting tasks. The R binding, at production maturity, integrates with R data.frames for efficient in-memory processing and IPC exchange in statistical computing workflows. All these bindings align with the Arrow IPC format (version 1.5.0 as of October 2025) for interoperability. Across bindings, the structure is consistent and modular. Builders allow programmatic construction of arrays and tables from native language types, such as appending values to create fixed-size or variable-length arrays. Readers and writers handle IPC for streaming data between processes or persisting to disk in Arrow's binary format. Basic compute kernels, including filter (for masking), sort (by key with options for ascending/descending), and arithmetic operations, are available in most implementations to perform in-memory transformations without external dependencies.

Interoperability Mechanisms

Apache Arrow facilitates interoperability through standardized protocols and mechanisms that enable efficient, exchange between diverse systems, languages, and processes without overhead. By defining a common in-memory columnar format, Arrow allows to be shared directly as buffers, minimizing copies and maximizing performance across analytical pipelines. A primary mechanism is the Flight protocol, a gRPC-based RPC framework designed for high-performance services using Arrow's IPC format. It supports streaming queries via methods like DoGet for downloading and DoPut for uploading, along with through token-based mechanisms such as or custom headers. This enables low-latency transfers over networks, suitable for distributed systems. Building on Flight, Arrow Flight SQL extends the protocol to support SQL interactions with databases, allowing clients to execute queries and retrieve results in Arrow format. It enables federated queries across Arrow-compatible databases by defining SQL-specific commands like GetSqlInfo and ExecuteSql, promoting seamless integration without custom connectors. For instance, in DuckDB lakehouse setups, Arrow Flight SQL is used for efficient remote querying, enabling high-performance data transfer and federated queries across services without ETL processes. For local interoperability, Arrow leverages transports to achieve access in-process and multi-process scenarios. On systems, it uses memory-mapped files via mechanisms like for efficient buffer sharing, while on Windows, equivalent file mapping APIs support the same relocatable buffer design. This allows data larger than available RAM to be processed through on-demand paging across languages and processes. Arrow also integrates directly with popular dataframe libraries for seamless conversions. In Python, PyArrow provides zero-copy mappings to DataFrames via methods like Table.to_pandas() and Table.from_pandas(), preserving data types and enabling efficient analytical workflows. Similarly, the Arrow R package converts between data.frames and Arrow Tables, supporting read/write operations with minimal overhead. For Julia, Arrow.jl offers integration with DataFrames.jl, allowing direct serialization and deserialization of dataframes to Arrow format for cross-language compatibility. In the vendor ecosystem, Arrow powers native read/write capabilities in tools like Dremio, which loads data from sources such as S3 or RDBMS into Arrow buffers for accelerated SQL querying via ODBC/JDBC. Tableau utilizes Arrow through plugins like pantab for high-performance exchange with its Hyper database, facilitating dataframe imports from Pandas or PyArrow. AWS Athena employs Arrow in federated queries, where connectors return results in Arrow format to enable efficient data retrieval from diverse sources without intermediate serialization.

Applications and Integrations

Key Use Cases

Apache Arrow's columnar in-memory format enables efficient analytical workloads by facilitating zero-copy data sharing and vectorized processing in (ETL) pipelines. In tools like , it supports schema-on-read queries across diverse data sources without requiring upfront ETL transformations, allowing for low-latency ad-hoc analysis on large datasets stored in formats such as or CSV. This integration reduces data movement overhead, enabling to process petabyte-scale data directly in memory for faster query execution compared to traditional row-based approaches. In workflows, Apache Arrow accelerates data loading and preprocessing by providing a standardized interface for datasets compatible with frameworks like and . For instance, 's tf.data API leverages Arrow datasets to ingest columnar data with minimal serialization overhead, supporting efficient batching and shuffling for training large models. Similarly, libraries such as Petastorm use Arrow to read files directly into tensors, enabling scalable distributed training on massive datasets without intermediate conversions, which can improve I/O throughput by up to 10x in certain benchmarks. For streaming analytics, Apache Arrow's Flight protocol facilitates real-time data interchange in systems like Apache Kafka and Apache Flink, where high-velocity event streams require low-latency serialization and deserialization. In Kafka, Arrow serializes columnar messages for efficient producer-consumer pipelines, allowing downstream applications to process streams without reformatting data. Flink integrates Arrow for in-memory representation of streaming data, optimizing stateful computations and windowed aggregations by reducing memory copies during operator chaining. This setup supports sub-second query latencies in real-time dashboards and fraud detection use cases. Apache Arrow enhances data visualization through zero-copy transfers to tools such as Tableau and Power BI, enabling interactive exploration of large datasets without loading entire tables into memory. In Tableau, the pantab library uses Arrow to export DataFrames directly as Hyper extracts, streamlining data preparation for dashboards that handle millions of rows. Power BI employs the Arrow Database Connectivity (ADBC) driver for querying Arrow-compatible sources like , which minimizes transfer times and supports direct visualization of analytical results in reports. Within ecosystems, Apache Arrow plays a central role in for columnar caching and vectorized user-defined functions (UDFs), particularly in PySpark, where it optimizes DataFrame-to-Pandas conversions to avoid bottlenecks. This integration allows Spark to leverage Arrow's memory layout for faster Python interoperability, achieving up to 5x performance gains in group-by operations on terabyte-scale data. In , Arrow serves as the backend for out-of-core processing via the pyarrow engine, enabling efficient handling of datasets larger than available RAM through memory-mapped files and , which is crucial for in resource-constrained environments.

Comparison with Parquet and ORC

Apache Arrow serves primarily as an in-memory columnar format optimized for efficient and interchange across languages and systems, whereas and are designed as on-disk storage formats emphasizing compression and query optimization for large-scale analytics. Arrow enables zero-copy access and direct CPU vectorization without deserialization overhead, making it suitable for RAM-bound workloads, while and incorporate advanced encoding and compression techniques that reduce storage footprint but require decompression during reads. These differences stem from their core purposes: Arrow focuses on computational portability, on broad analytical efficiency, and on Hadoop ecosystem integration. In comparison to , prioritizes in-memory performance through its standardized layout that aligns data for SIMD instructions and avoids the encoding/decoding steps inherent in Parquet's columnar storage. Parquet excels in on-disk scenarios with superior compression ratios—often achieving 13% of original data size through dictionary and —and supports predicate pushdown for efficient column pruning during scans. However, is frequently layered atop Parquet for storage, where Parquet files are read into 's in-memory representation for faster subsequent processing, as acts as an ideal in libraries like PyArrow. Benchmarks show providing 2-4x faster direct querying on loaded data compared to Parquet's transcoding requirements, though Parquet's data skipping can outperform in selective disk reads. Arrow contrasts with ORC by offering a inter-process communication (IPC) protocol that facilitates seamless across tools, unlike ORC's more Hadoop-centric design with built-in lightweight indexes for bloom filters and min-max statistics. ORC provides strong compression (around 27% of original size) and efficient in-memory mapping, particularly for projection operations on integers, but incurs higher decompression costs (2-3x longer than in some cases). Arrow's focus on compute portability enables its use as an in-memory layer for ORC files, similar to Parquet integrations, allowing systems to leverage ORC's archival strengths while benefiting from Arrow's low-latency access. Performance trade-offs highlight Arrow's advantages in memory-intensive analytics, where it can deliver up to 4x speedups over row-oriented formats and faster reads than compressed disk formats like or due to eliminated ser/de overhead; for instance, in TPC-DS benchmarks, leads overall query times thanks to skipping, but Arrow shines in post-load operations. Conversely, Arrow lacks and 's disk optimizations such as fine-grained column and heavy compression, resulting in larger in-memory footprints without encoding (up to 107% of raw size in uncompressed cases). These formats are often complementary: Arrow serves as an interchange layer on top of or files in ecosystems like and Presto, enabling efficient pipelines from storage to computation. Arrow is preferable for cross-tool data pipelines requiring rapid in-memory sharing and zero-copy transfers, such as real-time analytics or federated queries, while suits write-once archival storage with broad ecosystem support, and is ideal for read-heavy Hadoop workloads with integrated indexing.

Governance and Community

Project Governance

Apache Arrow is a top-level project within (ASF), accepted directly as such on February 17, 2016, following its initial proposal earlier that year. Unlike most ASF projects that undergo an , Arrow bypassed the incubator due to its established codebase seeded from contributions across multiple Apache projects, such as and . The project operates under the ASF's consensus-driven governance model, emphasizing community-led development free from commercial influence, with decisions made through open discussion and lazy consensus on mailing lists. The Project Management Committee (PMC) serves as the governing body for Apache Arrow, comprising 62 members from diverse organizations, including chair Neal Richardson of Posit and Antoine Pitrou of QuantStack. PMC members are selected based on their sustained contributions and leadership, with the committee holding authority over key decisions such as approving project releases, inviting new committers, and nominating additional PMC members. Committers, who have write access to the repositories, are onboarded by the PMC after demonstrating high-quality, ongoing involvement in areas like code development, reviews, , or , typically over a period of several months. The release process for Apache Arrow follows the ASF's formal policy, with adherence to Semantic Versioning (SemVer) for API stability beginning with version 1.0.0 in 2020, where major releases introduce breaking changes, minor releases add features, and patch releases include bug fixes. Proposed changes are tracked and discussed via JIRA issues, with significant updates requiring community consensus on the [email protected] mailing list. Release candidates undergo verification on multiple platforms before a formal vote, needing at least three binding +1 votes from PMC members and no vetoes to proceed to distribution. Contributions to the project are guided by established ASF and Arrow-specific policies to ensure quality and inclusivity. All contributors must sign either an Individual Contributor License Agreement (ICLA) or Corporate CLA (CCLA) to grant the ASF rights to their work under the Apache License 2.0. The community enforces the Apache Code of Conduct, promoting respectful interactions, consensus-building, and merit-based recognition. For code contributions, developers follow a branching strategy where new features are integrated into the main branch prior to a feature freeze; post-freeze, only bug fixes and security updates are permitted on dedicated maintenance branches (e.g., maint-15.0.0) to maintain stability during release cycles. Pull requests are reviewed collaboratively, with committers merging approved changes after ensuring tests pass and documentation is updated.

Adoption and Ecosystem

Apache Arrow's contributor base has expanded significantly, with over 100 active committers affiliated with prominent organizations including , , , Dremio, and Apple. This growth underscores the project's appeal across industry and academia, evolving from around 20 committers in 2017 to the current robust community of approximately 700 contributors submitting thousands of pull requests annually. The ecosystem surrounding Apache Arrow extends far beyond its core libraries, enabling seamless integrations in modern data tools and platforms. For instance, DuckDB leverages Arrow for zero-copy data exchange with Polars DataFrames, allowing efficient querying of in-memory datasets without serialization overhead. In lakehouse setups, DuckDB utilizes Arrow Flight SQL for efficient remote querying, enabling high-performance data transfer from remote databases and storage systems. Similarly, Polars utilizes as its foundational format for high-performance data manipulation, compatible with libraries like . In cloud environments, Google BigQuery supports Arrow for exporting query results, facilitating faster data transfer to analytical workflows. Industry adoption of Apache Arrow spans diverse sectors, enhancing efficiency in high-stakes applications. Healthcare benefits from Arrow's columnar format in processing genomic datasets and patient records, as seen in integrations with tools like Vaex for exploratory analysis. In AI pipelines, Arrow accelerates and model training by providing a unified interchange layer, with reported speedups in ETL workflows. The Arrow community fosters collaboration through dedicated events and working groups. Participants engage at ApacheCon, where sessions cover Arrow's advancements in , and specialized gatherings like Arrow Dev Days, which focus on developer deep dives into implementation challenges. Working groups, such as the one for C++ compute kernels, drive extensions for advanced analytical functions, ensuring cross-language consistency. (ApacheCon context) Metrics highlight Arrow's impact in the landscape, with its repository amassing over 5,500 stars and 3,500 forks as of late 2025, indicating strong developer interest. Download trends for the Python Arrow package exceed 15 million monthly via PyPI, reflecting widespread adoption in analytical stacks.

References

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  5. https://arrow.apache.org/release/
  6. https://arrow.apache.org/powered_by/
  7. https://news.apache.org/foundation/entry/the_apache_software_foundation_announces87
  8. https://www.dremio.com/blog/the-origins-of-apache-arrow-its-fit-in-todays-data-landscape/
  9. https://arrow.apache.org/blog/2017/07/26/spark-arrow/
  10. https://github.com/apache/spark/pull/15821
  11. https://wesmckinney.com/blog/apache-arrow-pandas-internals/
  12. https://arrow.apache.org/release/1.0.0.html
  13. https://arrow.apache.org/blog/2020/10/22/2.0.0-release/
  14. https://arrow.apache.org/docs/format/Flight.html
  15. https://arrow.apache.org/release/22.0.0.html
  16. https://arrow.apache.org/docs/format/Columnar.html
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  18. https://arrow.apache.org/docs/format/CDataInterface.html
  19. https://arrow.apache.org/docs/python/memory.html
  20. https://arrow.apache.org/docs/cpp/memory.html
  21. https://arrow.apache.org/docs/cpp/compute.html
  22. https://arrow.apache.org/docs/python/index.html
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  26. https://arrow.apache.org/docs/format/FlightSql.html
  27. https://arrow.apache.org/blog/2022/02/16/introducing-arrow-flight-sql/
  28. https://voltrondata.com/blog/serving-dataframes-over-the-wire-with-arrow-flight-sql-and-duckdb
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  32. https://arrow.apache.org/docs/python/pandas.html
  33. https://arrow.apache.org/docs/r/
  34. https://arrow.apache.org/julia/
  35. https://docs.aws.amazon.com/athena/latest/ug/federated-queries.html
  36. https://www.dremio.com/wiki/apache-arrow/
  37. https://blog.tensorflow.org/2019/08/tensorflow-with-apache-arrow-datasets.html
  38. https://github.com/uber/petastorm
  39. https://medium.com/towards-data-engineering/exploring-apache-arrow-a-modern-framework-for-efficient-data-interchange-a0d91e334a01
  40. https://github.com/innobi/pantab
  41. https://learn.microsoft.com/en-us/azure/databricks/partners/bi/power-bi-adbc
  42. https://spark.apache.org/docs/latest/api/python/tutorial/sql/arrow_pandas.html
  43. https://arrow.apache.org/committers/
  44. https://www.apache.org/foundation/how-it-works.html
  45. https://cwiki.apache.org/confluence/display/ARROW/Inviting+New+Committers+and+PMC+Members
  46. https://www.apache.org/legal/release-policy.html
  47. https://arrow.apache.org/docs/developers/release.html
  48. https://arrow.apache.org/docs/developers/release_verification.html
  49. https://www.apache.org/licenses/contributor-agreements.html
  50. https://www.apache.org/foundation/policies/conduct.html
  51. https://www.dremio.com/blog/apache-arrows-rapid-growth-over-the-years/
  52. https://duckdb.org/docs/stable/guides/python/polars.html
  53. https://www.hopsworks.ai/post/python-centric-feature-service-with-arrowflight-and-duckdb
  54. https://dev.to/mikekenneth77/unlocking-duckdb-from-anywhere-a-guide-to-remote-access-with-apache-arrow-and-flight-rpc-grpc-439p
  55. https://docs.pola.rs/user-guide/ecosystem/
  56. https://dataintellect.com/blog/apache-arrow-duckdb-polars-and-vaex/
  57. https://arrow.apache.org/community/
  58. https://github.com/apache/arrow
  59. https://www.datacamp.com/blog/top-python-libraries-for-data-science
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