Rust Data Engineering: Building High-Performance EDA

In this project, we will develop a Rust-based system to analyze and transform climate change data. We'll be using the Polars library to work with a powerful DataFrame interface that supports efficient data manipulation and querying. The system will involve handling large datasets, performing various data transformations, and applying different operations to explore and understand climate change trends. 

By integrating data from CSV files, processing it through a series of transformations, and performing complex join operations, we will create a robust framework for climate data analysis.

We’ll implement functions to load data, perform calculations, and produce meaningful insights, while also ensuring efficient processing through concurrent operations. We'll do all this in the Rust programming language due to its strong performance characteristics, memory safety guarantees, and efficient concurrency handling. These features make it ideal for processing large datasets and performing complex transformations with minimal overhead and reduced risk of runtime errors. Rust's robust tooling and compile-time checks also ensure reliable and maintainable code.