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

For teams building LLM systems, the main takeaway is that data engineering has become an application-layer discipline, not a preprocessing task. The book’s structure reflects a practical shift: raw collection, cleaning, alignment, retrieval, and synthetic generation all sit on the critical path to model behavior. That means architecture choices in storage formats, distributed processing, and dataset lineage now directly affect training stability and downstream product quality.

The most operationally important theme is reproducibility. Once corpora are assembled from noisy sources, teams need ways to version datasets, track transformations, and compare iterations across experiments. Tools such as DVC, LakeFS, and object-store-friendly formats are not incidental add-ons; they are the controls that make large-scale curation auditable and reusable across pre-training, fine-tuning, and RAG pipelines.

There is also a clear integration challenge across modalities and stages. Text, image, video, audio, and retrieval data each impose different parsing, chunking, and alignment requirements, so the same orchestration layer rarely fits all workloads. In practice, this pushes teams toward modular pipelines with explicit interfaces between extraction, enrichment, deduplication, embedding, and indexing steps.

For engineering leaders, the hidden trade-off is cost versus data fidelity. More aggressive filtering can improve quality but may remove rare or domain-specific examples that matter for specialization. Synthetic data can fill gaps, yet it increases the need for validation and provenance checks. The useful architectural question is not whether to build a data pipeline, but how to make it measurable, replaceable, and safe to iterate.

“Data is the new oil, but only if you know how to refine it.”

In the era of large models, data quality determines the upper bound of model performance. Yet systematic resources on LLM data engineering remain extremely scarce — most teams are still learning by trial and error.

This book is designed to fill that gap. We systematically cover the complete technical stack from pre-training data cleaning to multimodal alignment, from RAG retrieval augmentation to synthetic data generation, including:

  • Pre-training Data Engineering: Extracting high-quality corpora from massive noisy data sources like Common Crawl
  • ️ Multimodal Data Processing: Collection, cleaning, and alignment of image-text pairs, video, and audio data
  • Alignment Data Construction: Automated generation of SFT instruction data, RLHF preference data, and CoT reasoning data
  • RAG Data Pipeline: Enterprise-grade document parsing, semantic chunking, and multimodal retrieval

Beyond in-depth theoretical explanations, the book includes 5 end-to-end capstone projects with runnable code and detailed architecture designs for hands-on learning.

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

A complete data engineering pipeline from raw data to end-to-end applications

 6 Parts, 13 Chapters + 5 Capstone Projects
│
├── Part 1: Infrastructure & Core Concepts
│   ├── Chapter 1: Data Revolution in the LLM Era
│   └── Chapter 2: Data Infrastructure Selection
│
├── Part 2: Text Pre-training Data Engineering
│   ├── Chapter 3: Data Acquisition
│   ├── Chapter 4: Cleaning & Deduplication
│   └── Chapter 5: Tokenization & Serialization
│
├── Part 3: Multimodal Data Engineering
│   ├── Chapter 6: Image-Text Pair Processing
│   ├── Chapter 7: Recaptioning
│   └── Chapter 8: Video & Audio Data
│
├── Part 4: Alignment & Synthetic Data Engineering
│   ├── Chapter 9: Instruction Fine-tuning Data
│   ├── Chapter 10: Synthetic Data
│   └── Chapter 11: Human Preference Data
│
├── Part 5: Application-level Data Engineering
│   ├── Chapter 12: RAG Data Pipeline
│   └── Chapter 13: Multimodal RAG
│
└── Part 6: Capstone Projects
    ├── Project 1: Building Mini-C4 Pre-training Set
    ├── Project 2: Domain Expert SFT (Legal)
    ├── Project 3: Building LLaVA Multimodal Instruction Set
    ├── Project 4: Synthetic Math/Code Textbook
    └── Project 5: Multimodal RAG Financial Report Assistant
  • Data-Centric AI philosophy throughout
  • Covers the full LLM data lifecycle: Pre-training → Fine-tuning → RLHF → RAG
  • In-depth coverage of Scaling Laws, data quality evaluation, multimodal alignment, and more
Domain Technologies
Distributed Computing Ray Data, Spark
Data Storage Parquet, WebDataset, Vector Databases
Text Processing Trafilatura, KenLM, MinHash LSH
Multimodal CLIP, ColPali, img2dataset
Data Versioning DVC, LakeFS
Project Core Technologies Output
Mini-C4 Pre-training Set Trafilatura + Ray + MinHash High-quality text corpus
Legal Expert SFT Self-Instruct + CoT Domain instruction dataset
LLaVA Multimodal Bbox alignment + multi-image interleaving Visual instruction dataset
Math Textbook Evol-Instruct + sandbox verification PoT reasoning dataset
Financial Report RAG ColPali + Qwen-VL Multimodal QA system
  • Python 3.8+
  • MkDocs Material
  • mkdocs-static-i18n (i18n support)
# Clone the repository
git clone https://github.com/datascale-ai/data_engineering_book.git
cd data_engineering_book

# Install dependencies
pip install mkdocs-material mkdocs-glightbox pymdown-extensions "mkdocs-static-i18n[material]"

# Local preview
mkdocs serve

Visit http://127.0.0.1:8000 to preview the book (with Chinese/English language switcher).

The generated static files are located in the site/ directory.

data_engineering_book/
├── docs/
│   ├── zh/                  # Chinese content
│   │   ├── index.md         # Chinese homepage
│   │   └── part1/ ~ part6/  # All chapters
│   ├── en/                  # English content
│   │   ├── index.md         # English homepage
│   │   └── part1/ ~ part6/  # All chapters
│   ├── images/              # Image assets (shared)
│   ├── stylesheets/         # Custom styles
│   └── javascripts/         # JavaScript (MathJax etc.)
├── .github/workflows/       # GitHub Actions CI/CD
├── mkdocs.yml               # MkDocs configuration
├── 框架图.png                # Book architecture diagram
├── LICENSE                  # License
├── README.md                # 中文说明
└── README_en.md             # English README (this file)
  • LLM R&D Engineers
  • Data Engineers / MLOps Engineers
  • AI Product Managers (Technical)
  • Researchers interested in LLM data pipelines

Contributions are welcome! Feel free to submit Issues and Pull Requests.

  1. Fork this repository
  2. Create a feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

This project is licensed under the MIT License – see the LICENSE file for details.

If you find this book helpful, please give it a Star! ⭐

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