加载中
正在获取最新内容,请稍候...
正在获取最新内容,请稍候...
本项目提供一个高性能的原生 Rust 库,用于读写 Delta Lake,并包含易于使用的 Python 绑定。它 enables efficient data processing without JVM overhead.
A high-performance native Rust library for interacting with Delta Lake, featuring robust Python bindings for broad accessibility and use.
Existing Delta Lake connectors often rely on the JVM or have performance limitations for certain workloads. This project aims to provide a native, high-performance alternative in Rust with convenient Python access.
Leverage Rust's performance for fast read and write operations on Delta Lake tables.
Seamlessly access Delta Lake functionalities directly from Python applications.
Supports essential Delta Lake features like schema evolution, time travel, and ACID transactions.
本项目可在多种场景下提供高效的 Delta Lake 交互能力:
Quickly load data into Delta Lake tables from various sources using native Rust or Python scripts for better performance.
Reduce data loading times and improve ingestion throughput.
Develop custom data processing applications or microservices in Rust that directly read from and write to Delta Lake without external dependencies like Spark.
Build lean, fast, and easily deployable data applications.
Utilize Python for analytics and machine learning workflows on Delta Lake, leveraging the performance of the underlying Rust implementation.
Enhance performance of data analysis scripts interacting with Delta Lake.
You might be interested in these projects
Discover AutoAgent, a cutting-edge framework that empowers users to build and deploy powerful LLM agents without writing a single line of code. Streamline complex workflows and automate tasks with intuitive, visual interfaces.
Git-Cliff is a powerful and flexible command-line tool designed to automate the process of generating changelogs from your Git history. It strictly follows the Conventional Commits specification and offers extensive customization options.
This project provides a robust and efficient solution for automating repetitive tasks and processing large datasets. Designed for ease of use and scalability, it helps individuals and teams significantly reduce manual effort and improve data accuracy.