Overview

Are you tired of babysitting fragile data pipelines that buckle under production loads? Do you crave the raw power to process terabytes of data with the reliability of a systems engineer, but feel held back by the memory overhead and runtime errors of interpreted languages? Data Engineering with Rust is the definitive guide for engineers ready to rewrite the rules of data infrastructure. This book moves beyond simple scripting to teach you how to architect systems where safety and speed are not trade-offs, but guarantees. It serves as a practical bridge, translating Rust's renowned systems-level performance into the world of ETL, streaming, and big data analytics. Through a hands-on, project-driven approach, you will master the art of building ""blazingly fast"" pipelines that are type-safe and memory-efficient by default. You will learn to harness Apache Arrow for zero-copy data exchange and leverage the Polars DataFrame library to perform complex transformations at speeds that leave legacy tools in the dust. The narrative guides you through constructing concurrent, fault-tolerant microservices capable of ingesting high-throughput streams from Kafka and serving Machine Learning models with sub-millisecond latency. By the end, you will possess the skills to design infrastructure that handles massive scale without the constant fear of unhandled exceptions. Stop compromising on performance. Upgrade your toolkit, eliminate technical debt, and engineer the future of your data infrastructure today.

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