#Apache Arrow

DataFusion is a Rust-based query engine integrated with Apache Arrow. It provides libraries for building database and analytics systems tailored to specific workloads. Supporting SQL and DataFrame APIs, DataFusion offers performance with native support for formats like CSV, Parquet, JSON, and Avro. Its design allows customization, including new data sources and functions, and supports Python and distributed integrations for diverse use cases.

Discover Polars, a high-performance DataFrame library built with Rust and Apache Arrow, suitable for efficient data processing across Python, Node.js, R, and SQL. Features include multi-threading, query optimization, and a powerful expression API. Designed to manage datasets larger than RAM with fast, streaming execution for lightweight, dependency-free performance, Polars is perfect for advanced data analysis in multiple programming environments.

ROAPI enables the creation of read-only APIs for static datasets without requiring coding. Utilizing Apache Arrow and Datafusion, it translates various query types including SQL, FlightSQL, GraphQL, and REST APIs into executable plans, supporting multiple data sources and formats. The tool automatically infers schemas and encodes responses in various formats, suitable for seamless data processing. It supports RESTful, GraphQL, and SQL interfaces, providing functionalities such as table joins and Postgres protocol query capability, ideal for diverse platform integration.

This Python-based data catalog uses Ray for efficient management of scalable, ACID-compliant databases. It features git-like workflows for managing exabyte-scale data lakes and applies Apache Arrow for tasks like change-data-capture and table repair, maintaining data consistency and integrity.