I'm currently a Master's student at USTC (University of Science and Technology of China). I've been diving deep into Data Engineering, especially in the context of Large Language Models (LLMs).

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

I hope xx123122 won't mind my mentioning that they emailed us about this post, which originally got caught in a spam filter. I invited them to post a comment giving the background to the project but they probably haven't seen my reply yet. Hopefully soon, given that the post struck a chord!

Edit: they did, and I've moved that post to the toptext.