Rust For Data Engineering

Sixteen cores. Fifteen of them idle. One of them running pandas.

Every data engineer knows this wall. The file will not fit in memory, so you split it by hand. The job takes six hours and needs to take one. You provision a larger instance to solve what is really a memory-layout problem, and the bill arrives at the end of the month as a reminder that you did not actually fix anything.

This is not a slow-function problem. It is a structural one. And no amount of Python tuning gets you past it.

RUST FOR DATA ENGINEERING is for data engineers who have hit that wall and want the tools that go through it, not around it.

NOT A GENERAL RUST BOOK

This book does not teach web servers, game engines, or embedded systems. It teaches the parts of Rust that matter when datasets become larger than memory: streaming, columnar storage, zero-copy processing, and using every CPU core you paid for.

No systems programming background is required. Every concept-from memory layout to data races-is introduced exactly when needed, using language a Python data engineer already understands.

WHAT YOU WILL BE ABLE TO DO

HOW THE BOOK IS BUILT

PART 1 (Ch. 1-4) Why these performance walls exist, plus the Rust fundamentals that matter for data engineering.
PART 2 (Ch. 5-9) Streaming massive files, CSV, JSON, Parquet, and Apache Arrow.
PART 3 (Ch. 10-12) Filtering, aggregation, joins, Polars, and resilient pipeline design.
PART 4 (Ch. 13-15) Rayon, true parallelism, streaming with backpressure, and a complete production-style capstone project.

15 chapters. 228 pages. 32 figures. Every chapter ends with a quiz, and every answer is fully explained in Appendix A.

THE CAPSTONE

Chapter 15 builds a complete high-volume pipeline from ingestion to output, combining streaming, parallel processing, error recovery, and production-ready architecture instead of simplified toy examples.

READ IT WITH A REAL DATASET IN MIND

Every technique in this book solves problems that only appear at scale. The lessons become immediately practical when applied to the dataset that has been slowing your work.

PREREQUISITES

Working knowledge of data engineering: reading files, transforming records, joining datasets, and building pipelines. No Rust. No C. No systems programming background required.

Part of the RUST IN PRACTICE series from Waskey Press. Every volume stands completely alone-no previous volume required.

Scroll up and start processing data at the scale your hardware was always capable of.