Polars is a fast columnar DataFrame engine built on Apache Arrow, and this course teaches you to use it from Rust to do real data-engineering work. You will configure a Cargo project with the lazy and csv feature flags, load wine-ratings.csv into a typed DataFrame, and learn the difference between eager DataFrames for exploration and lazy LazyFrames for production. You will compose select, filter, slice, sort, group_by, agg, and join expressions, then read explain output to see predicate pushdown and projection pushdown rewrite your query before it runs. Module 2 puts the API to work cleaning a real wine-ratings dataset with documented drop, fill, and normalize rules. Module 3 wires everything into wine-pipeline, three Rust CLI binaries that implement a bronze, silver, gold medallion architecture over a shared SQLite database and export a top-10 grape leaderboard as CSV and JSON. By the end you will have a complete, runnable Rust pipeline you can adapt to any tabular dataset.