Apache Arrow is an open-source columnar memory format for both flat and hierarchical data. In the modern datalake, open data formats, like Apache Arrow, live in the storage layer in modern object storage. These formats become the objects in object storage.

In their most recent release, Apache Arrow announced plans to separate from Apache DataFusion, the query execution framework that has been attached to Arrow since February 2019, and to graduate both projects to top level within the Apache Software Foundation. The contributors explain that as both projects have grown they have differentiated and while DataFusion continues to rely on Arrow, the reverse cannot be said. This move represents Arrow’s maturity and widespread adoption in the data community. This blog aims to highlight these contributions.

Understanding Apache Arrow

The Arrow format is designed to optimize data processing and analytic operations across various data systems. Meaning that Arrow is designed to work with many different processing engines, which is essential for data lakes that handle vast amounts of complex, semi-structured data with many different use cases.

Apache Arrow is extremely performant, largely due to its columnar data format which minimizes the need for data serialization and deserialization. This format not only facilitates faster data access but also supports real-time analytics on data lakes. Additionally, Arrow’s use of memory mapping allows datasets to be backed by an on-disk cache that is memory-mapped for fast data retrieval. This feature is especially effective in environments with limited RAM, enabling the handling of large datasets efficiently. These attributes make Arrow a fundamental component in modern data architectures, particularly in enhancing interoperability and computational efficiency within diverse data environments.

Key Benefits of Apache Arrow

Open-source: We have long supported openness in the modern data stack. In large part, because open-source begets open-source as collaboration drives forward innovation. This is particularly true of open standards like Apache Arrow because they play a crucial role in accelerating innovation within the data ecosystem. By providing a common framework for interoperability, open standards empower developers to collaborate more effectively and avoid redundant efforts in reinventing solutions. This, in turn, fosters a culture of innovation where ideas can be shared and built upon, driving continuous progress and evolution.

Performance: By adopting Arrow, organizations can exchange data seamlessly between different systems without incurring the performance costs associated with serialization and deserialization. There is of course nothing that compliments performance like performance.

Simplified Integration: The standardization offered by Arrow reduces the complexity of integrating disparate tools, allowing developers to focus on building robust solutions rather than grappling with integration challenges. Cloud-native projects, frameworks and software work together out of the box by design. 

Notable Projects Embracing Apache Arrow

Apache Arrow has garnered widespread adoption across various projects. We’ve written about a few already, including integrations with Spark, and R, but there are quite a few more including but not limited to:

1. Polars: is a blazingly fast DataFrame library in Rust that leverages Arrow's columnar storage format for efficient data processing, enhancing performance and scalability. The integration of Polars with Apache Arrow reinforces the foundation of modern data lake infrastructures, enabling high-speed data operations and analytics.
2. DuckDB: integrates seamlessly with Apache Arrow for efficient data interchange, enabling fast data transfer and analysis. This integration plays a pivotal role in modern data lake infrastructures, facilitating rapid data processing and query execution across diverse datasets.
3. ClickHouse: is an open-source analytical database management system known for its high performance in real-time query processing. It leverages Apache Arrow to enhance several aspects of its operations, primarily focusing on data import and export, as well as enabling direct querying capabilities.
4. PySpark: harnesses Apache Arrow's columnar data representation for efficient data processing, enhancing performance and scalability. The seamless integration of PySpark with Apache Arrow underpins modern data lake infrastructures, enabling organizations to build robust and scalable data processing pipelines with ease.
5. Pandas: benefits from Arrow's efficient memory layout and interoperability, enabling seamless data exchange with other systems and languages in the modern datalake stack. 
6. Ray: is a distributed computing framework, that leverages Apache Arrow for efficient data serialization and transfer between distributed tasks. This integration enhances Ray's performance and scalability, enabling users to build and deploy distributed applications with ease.
7. delta-rs: is an open-source Rust library that provides a native Rust implementation for Delta Lake. Delta-rs uses Arrow to store and manage data internally, which enables quick, efficient operations on Delta Lake tables, particularly when processing large datasets.
8. iceberg-arrow: is an Iceberg Table support library that allows for reading Parquet into Arrow memory. It has performance that is equal to or better than the default Parquet vectorized reader.
9. Hugging Face Datasets uses Arrow for its on-disk cache system, which allows large datasets to be stored locally on systems with limited memory. The on-disk cache is memory-mapped for efficient lookups.
10. RAPIDS: is a suite of open-source libraries for GPU-accelerated data science and analytics, that utilizes Apache Arrow for interoperability between GPU-accelerated data processing tasks. This integration enables RAPIDS to leverage Arrow's efficient columnar format for high-speed data processing on GPUs.

While these projects represent a subset of the vast ecosystem embracing Apache Arrow, they exemplify the versatility and adaptability of the standard across different domains and use cases.

Open Source Standards 

Apache Arrow stands as a testament to the power of open standards in driving interoperability and innovation within the modern datalake. As organizations continue to harness the capabilities of open standards in their stack, the potential for transformative advancements in AI and analytics remains boundless. Let us know what you’re building at hello@min.io or on our Slack channel.