High-Performance Data Engineering: Integrating Python and Rust

This course teaches advanced data engineers how to build hybrid systems that combine Python’s flexibility with Rust’s speed and memory safety for high-performance data processing.

Course Overview

Modern data engineering demands both rapid development cycles and extreme performance. Python excels at rapid prototyping and has a rich ecosystem of data science libraries. Rust provides memory safety without garbage collection, zero-cost abstractions, and performance comparable to C++.

By combining these languages, you can build data pipelines that are:

  • Fast: Leverage Rust’s zero-cost abstractions and SIMD optimizations
  • Safe: Memory safety guaranteed at compile time, no null pointer exceptions
  • Flexible: Python’s expressive syntax for orchestration and analysis
  • Scalable: Handle billions of rows with columnar storage and parallel processing

Syllabus

Chapter Title Description
Chapter 1 The Case for Rust in Data Engineering Why Python alone isn’t enough and how Rust fills the gap
Chapter 2 Columnar Storage from Scratch in Rust Building a type-safe columnar data engine with parallel compute kernels
Chapter 3 Bridging Rust and Python with ctypes FFI bindings, memory ownership, and designing Pythonic wrappers
Chapter 4 Apache Arrow: Zero-Copy Interoperability Using Arrow IPC to transfer data between Rust and Python without copying
Chapter 5 Parallelism Without the GIL Rayon data-parallel operations that bypass Python’s Global Interpreter Lock
Chapter 6 Async I/O with Tokio Concurrent data ingestion from multiple sources using Tokio
Chapter 7 Building the ETL Pipeline Practical examples of extraction, transformation, loading, and benchmarks
Chapter 8 Testing and Reliability Unit, integration, and property-based testing for hybrid Rust+Python systems
Chapter 9 Profiling and Optimization Flamegraphs, SIMD tuning, and memory profiling
Chapter 10 Interactive Data Engineering Jupyter notebooks for exploratory data analysis with the hybrid engine

Prerequisites

  • Intermediate Python programming
  • Basic understanding of systems programming concepts
  • Familiarity with data engineering concepts (ETL, dataframes, schemas)

What You’ll Build

By the end of this course, you will have:

  1. A working Rust library with C-compatible FFI exports
  2. A Python package that loads and uses the Rust library via ctypes
  3. A complete ETL pipeline demonstrating the hybrid architecture
  4. Interactive Jupyter notebooks for data exploration

About the Author

This course was written by Alberto Cardenas, a data engineer and systems architect specializing in high-performance data infrastructure. Alberto bridges the gap between Python’s expressive ecosystem and Rust’s systems-level performance.

License

This course material is provided for educational purposes.

Table of contents

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