Exploring Rust for Data Engineering with Polars
I've been living in the Python ecosystem for years now - handling DataFrames, visualizing with Matplotlib, and building neural networks in PyTorch. It's the usual toolkit that makes data science and machine learning so approachable. But last week, I decided to step out from this comfort zone and try a new language. I picked up Rust, a language I had only brushed against years ago when I first learned C++.
Like many of us, I often get these sudden urges to do something, to learn something but it fades away. This time, I gave myself a goal: learn enough Rust to use it in my ML pipelines if I ever wanted to. To keep myself accountable, I set up an experiment - I gave myself a data engineering pipeline in Rust using the Polars library.
The Project: High-Performance Data Engineering
The dataset I chose revolved around climate change trends. The tasks that I gave myself to learn included:
Loading large CSV files
Performing joins and concatenations
Reshaping data with pivots and melts
Parsing dates and running statistical calculations
Applying transformations to explore patterns
Essentially, it was a full end-to-end EDA pipeline, except I wasn't using Pandas but Polars - a blazing-fast DataFrame library that feels familiar but is powered by Rust's performance and memory safety guarantees.
Writing in Rust
Jumping into Rust after years of Python was challenging yet refreshing. Before this project, my only exposure to Rust came from casually watching a few videos on Rustfully. After looking at Rust documentations and hours of reading and watching how to write Rust code, I found an inherent joy in writing Rust.
From my old C++ days, I had to re-learn the humble
;at the end of every statement.Rust's quirks like adding a
?for error propagation or ending the function withOk(())genuinely made me chuckle at first but at the same time I was realizing how elegant and useful they are.One thing that I liked but I still cannot wrap my head around it is the borrow checker. It took me a while to realize how it worked and the confusion, and eventual realization made me think about ownership and references.
And how can I forget about the ally, Rust's error messages. Simply the best tutor - instead of just yelling at me, it guided me on how to solve it.
Where I See Rust Fitting
After the project, I realized how I could utilize Rust in my workflow:
Rust for pipelines and heavy data transformations. Tasks where concurrency, speed, and memory safety is crucial.
Python for ML. The ecosystem of libraries is unmatched.
The project is not complete yet. I'm still learning Rust, thinking about making a CLI tool for this project. As much as I love ML, I'd also like to dive deeper into systems programming and see what more Rust has to offer.
Closing Thought
This project reminded me of something important: curiosity and genuine interest often produce more joy than any "forced" assignment ever could. With this project, I am in no means an expert at Rust but learning it was absolutely amazing. I laughed at its quirks but also appreciated its attention to safety. It won't replace Python for me anytime soon, but Rust has definitely earned a spot in my toolkit for future data pipelines.
If you would like to view the project, it is available on my GitHub. Thank you for reading!
