Moving large amounts of data between Python and Rust is often slow and memory intensive because of unnecessary serialization and copying. As data pipelines, AI workloads, and analytics systems continue to grow, these overheads quickly become a bottleneck.

In this talk, we will explore how to achieve zero copy data sharing between Python and Rust using PyO3 and Apache Arrow. Starting from a simple Python and Rust integration, we will learn how Arrow’s columnar memory format allows both languages to work on the same data in memory without expensive conversions or duplication.

Through practical demos and real world examples, attendees will learn how to build high performance Python extensions in Rust, exchange Arrow arrays and tables efficiently, and significantly reduce memory overhead in data intensive applications. The session will also cover common interoperability pitfalls, debugging techniques, and performance comparisons between traditional serialization approaches and zero copy workflows.

By the end of the talk, attendees will understand how modern data systems use Arrow as a shared memory layer and how Python developers can leverage Rust for performance critical workloads without sacrificing developer productivity.