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Most data engineering courses stop at the export: a CSV lands in a folder, and someone else works out how to use it. The last step is often the one that fails. It gets rewritten from scratch, it quietly re-implements the cleaning rules, and it builds queries by gluing user input into strings.
Key Course Features:
- Load, inspect, cast and clean real data with Polars in Rust, eager first and then lazy
- Read the query plan Polars builds before it runs, and see predicate and projection pushdown for yourself
- Handle nulls deliberately: drop, fill or flag, in that order, and never invent data
- Build a bronze, silver and gold medallion pipeline as a single command-line tool
- Serve the gold layer over HTTP with Axum: filters, group-bys and search as Polars expressions
- Test every endpoint in-process, with no network and no data file, including the requests that must return nothing
Perfect for:
- Data engineers who can build a pipeline and now have to let other programs query its output
- Rust developers who want a practical, typed alternative to pandas for analytical work
- Backend engineers asked to put an API in front of a dataset without a database migration
You finish with one tool that ingests, cleans, aggregates and serves the same data, with a single definition of the gold layer shared by the export and the API.
