Let's start by getting an overview of the Rust programming language and its relevance within the data engineering space.
This chapter covers the basics of Rust, its core concepts and discusses how it compares to other programming languages commonly used for data engineering tasks and challenges.
The chapter also provides an overview of the Rust ecosystem and its tools and libraries for data processing and analysis. By the end of the chapter, you should have a foundational understanding of Rust and be able to start using it for small data engineering tasks.
We'll even dive into some code without having to install anything locally. It's perfect for trying things out and getting a hang of things without even leaving the browser.
Remember, this is not an academic course. Take breaks and enjoy the process :)
I'll try to set the stage for this guide and explain:
- What Rust is
- How Rust can help with data engineering tasks
- How Rust compares to Python and other languages for data engineering
- Why Rust might be a perfect choice to use for the future especially to reduce costs and increase maintainability of pipelines
- A few words on Rust’s efficiency and claims on it being a “eco friendly” programming language
- Speed is tough, rather a metric: How fast are Rust programs in average? Given a program, how easy is it to make it fast by using Rust defaults?