Data Engineering for LLMs: The Open-Source Guide to High-Quality Data Pipelines 🚀
In the era of Large Language Models (LLMs), we all know that "Data quality determines the model's upper limit." However, most developers are still "crossing the river by feeling the stones" when it comes to LLM data engineering. Finding systematic resources for data collection, cleaning, alignment, and RAG pipelines is surprisingly difficult. Many end up with datasets that are either low quality or impossible to deploy in production.
That’s why we created data_engineering_book — a one-stop open-source guide for LLM data engineering, covering architecture, algorithms, and real-world projects.
👉 GitHub: datascale-ai/data_engineering_book
👉 Live Docs: Read Online Here
🛠 Why This Project?
Current industry pain points are clear:
- Fragmented Knowledge: Tutorials are scattered across random blogs and papers.
- Model-Centric Bias: Too much focus on "fine-tuning parameters" while ignoring the Data-Centric AI core.
- Lack of Production Context: Theory is great, but how do you scale a cleaning pipeline to billions of tokens?
Our goal is to bridge this gap, helping you move from "using tools" to "building robust data lifecycles."
🏗 What’s Inside?
The handbook is structured into 6 parts, covering 13 chapters and 5 end-to-end production projects:
🗺 The Roadmap
- Part 1: Infrastructure & Core Concepts (Modern stack selection)
- Part 2: Text Pre-training (Scraping, Cleaning, Tokenization)
- Part 3: Multi-modal Data (Image-text pairs, Audio, Video)
- Part 4: Alignment & Synthetic Data (SFT, RLHF, and Synthetic generation)
- Part 5: Application-level Engineering (Advanced RAG pipelines)
- Part 6: Hands-on Projects (Runnable enterprise-level code)
💻 The Modern Tech Stack
We don't just talk theory. We focus on tools used in production today:
| Domain | Tech Stack |
|---|---|
| Distributed Computing | Ray Data, Apache Spark |
| Storage | Parquet, WebDataset, Vector DBs |
| NLP Processing | Trafilatura, KenLM, MinHash LSH |
| Multi-modal | CLIP, ColPali, img2dataset |
| Data Versioning | DVC, LakeFS |
🚀 Hands-on Projects You Can Run
The repo includes 5 full-stack projects with reusable code:
- Mini-C4 Construction: Build a pre-training dataset from scratch.
- Legal Expert SFT: High-quality instruction set generation for vertical domains.
- Multi-modal Instruction Sets: Building visual-language datasets.
- Synthetic Data Pipeline: Using LLMs to generate training data for LLMs.
- Multi-modal RAG: An enterprise-grade financial report assistant.
🌟 Support the Project
This is a community-driven project maintained by the datascale-ai team. It’s licensed under MIT and supports both English and Chinese.
If you find this resource helpful for your AI journey, we’d love your support:
- Star the Repo: datascale-ai/data_engineering_book ⭐️
- Contribute: Open an Issue or PR if you have better ideas for data cleaning or RAG optimization!
What is the biggest challenge you've faced in your LLM data pipeline? Let’s discuss in the comments! 👇
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