The Problem: I found that learning resources for modern data engineering are often fragmented and scattered across hundreds of medium articles or disjointed tutorials. It's hard to piece everything together into a coherent system.

The Solution: I decided to open-source my learning notes and build them into a structured book. My goal is to help developers fast-track their learning curve.

Key Features:

LLM-Centric: Focuses on data pipelines specifically designed for LLM training and RAG systems.

Scenario-Based: Instead of just listing tools, I compare different methods/architectures based on specific business scenarios (e.g., "When to use Vector DB vs. Keyword Search").

Hands-on Projects: Includes full code for real-world implementations, not just "Hello World" examples.

This is a work in progress, and I'm treating it as "Book-as-Code". I would love to hear your feedback on the roadmap or any "anti-patterns" I might have included!

Check it out:

Online: https://datascale-ai.github.io/data_engineering_book/

GitHub: https://github.com/datascale-ai/data_engineering_book