Welcome
Content
1.
Introduction to Rust for Data Engineering
1.1.
Introduction to Rust
1.2.
History of Rust
1.3.
Features of Rust
1.4.
Advantages of Rust for Data Engineering
1.5.
How does Rust compare to Python? (and other programming languages)
1.6.
Conclusion
2.
Getting started with Rust, developer environment setup and basics
2.1.
Setting up a Rust developer environment
2.2.
Configuring an IDE
2.3.
Creating your first programs (rustc)
2.4.
Working with Cargo
2.5.
Basic syntax
2.6.
Data types
2.7.
Functions, modules & error handling
2.8.
Rust specific features
2.9.
Crates & dependencies
2.10.
Rust for data in 15 minutes
3.
Working with Data in Rust, data modelling, working with CSV, JSON, serialising and data structures
3.1.
Basics of data modelling with Rust
3.2.
Reading, writing and appending to files
3.3.
Working with different data formats (parsing, serialising, writing)
3.3.1.
CSV
3.3.2.
JSON/JSONL
3.3.3.
Parquet
3.3.4.
Avro
3.3.5.
Protobuf
3.4.
Manipulating data in memory
3.4.1.
Apache Arrow
3.4.2.
Apache DataFusion
4.
Testing and debugging with Rust
5.
Parallel and concurrent programming in Rust
6.
Your first data pipelines with Rust: Extract data with API requests, scraping, parquet, avro, databases & SQLite
7.
Your first data pipelines with Rust: Transform data with pola.rs and Arrow DataFusion
8.
👩🏫 Your first data pipelines with Rust: Bringing it all together
9.
👩🏫 Simple data products with Rust: CLI job, create an API, process realtime data
10.
👩🏫 Advanced data products: benchmarking code, profiling and call Rust from Python with PyO3
11.
Rust for the future
Newsletter
🦀 Rust data Jobs 🦀
Changelog
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