Rust is a versatile, general-purpose programming language known for its emphasis on performance, type safety, and concurrency. A key feature of Rust is its enforcement of memory safety, ensuring all references are valid without the need for a garbage collector or reference counting, common in other memory-safe languages.
Its growing popularity in data engineering is noteworthy, primarily due to its type safety and efficient execution. While Rust might not completely replace Python’s dominance in DAG and data pipelines—owing to Python’s accessibility—it’s certainly making waves and could reshape future trends.
# Tools using Rust
- Notable Great Open-Source Tools in Rust.
- GitHub’s adoption of Rust for their new search feature: The technology behind GitHub’s new code search.
Refer to my experiences while Learning Rust.
Read more on my in-depth blog on Rust for Data Engineering.
# How to Get Started
Explore Rust’s impact in data engineering through resources like Polars for high-speed DataFrame operations ( https://lnkd.in/etKYF3Gc), the significant performance boost in pydantic using pydantic-core ( https://lnkd.in/e4xPznD9), the advanced in-memory analytics with PyArrow ( https://lnkd.in/eX6AZHfy), and the impressive delta-rs ( https://lnkd.in/edaf3-kF).
# Rust over Python
Rust streamlines development by catching errors like incorrect types at compile time, not at runtime. This eliminates the need for extensive external tooling (e.g., pytest, mypy, tox) required in Python. Cargo, Rust’s package manager, inherently provides this reliability and safety. While Python can be sufficiently fast and decisions between languages involve trade-offs, achieving similar reliability and safety in Python demands significant DevOps effort. This is a brief overview of the first chapter of my upcoming book RW (2) Post Feed LinkedIn.