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大模型数据工程：架构、算法及项目实战

1,206 starsPython

Data Engineering Book – An open source, community-driven guide

by xx123122·Feb 13, 2026·251 points·30 comments
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AI Analysis

●MidSolve My Problem

LLM-centric framing is smart, but curated guides already exist on HN weekly.

Strengths
  • •Scenario-based comparisons (Vector DB vs. Keyword Search) are more useful than tool lists.
  • •Hands-on projects with real code instead of tutorials-only approach.
  • •Covers modern stack: RAG, vector DBs, data quality for LLMs—timely topic.
Weaknesses
  • •Core format is static Markdown—no interactivity, notebooks, or executable exercises.
  • •Crowded category: dozens of free data engineering guides, newsletters, and courses already cover this.
Category
Education
Target Audience

Data engineers, ML engineers, students learning modern data stack and LLM workflows

Similar To

Databricks Learning Paths · Made With ML guides · Sebastian Raschka's ML systems design

Post Description

Hi HN! 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

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