Data Engineering for Large Foundation Models

A Handbook

English | 中文 | 日本語

Version note: The Chinese edition is the current 2026 Springer mainline, frozen at 14 parts, 48 chapters, 15 project case studies, and 8 appendices (A–H), with front matter and an afterword in the site edition. The English edition is being synchronized with a quality-first translation workflow; the documentation site includes an English edition status page.

Introduction

"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, all the way through DataOps platform engineering and privacy-compliant data governance, 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
• 🤖 Reasoning & Agent Data: Chain-of-thought, Tool-Use, multi-turn interaction, and memory data engineering
• 🔍 RAG Data Pipeline: Enterprise-grade document parsing, semantic chunking, and multimodal retrieval
• ⚙️ DataOps & Platform Engineering: Team organization, data versioning, and platform observability
• 🔒 Privacy, Compliance & Security: Data governance frameworks, federated learning, and privacy-enhancing technologies

Beyond in-depth theoretical explanations, the Chinese mainline includes 15 end-to-end hands-on project case studies with runnable code where available, architecture designs, and engineering retrospectives for hands-on learning.

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

Book Architecture

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

Table of Contents

📖 14 Parts, 48 Chapters + 15 Project Case Studies + 8 Appendices (A–H)
│
├── Part 1: Overview & Infrastructure (Ch01-Ch03)
├── Part 2: Text Pre-training Data Engineering (Ch04-Ch07)
├── Part 3: Multimodal Data Engineering (Ch08-Ch11)
├── Part 4: Instruction Fine-tuning & Preference Data (Ch12-Ch14)
├── Part 5: Synthetic Data Engineering (Ch15-Ch17)
├── Part 6: Reasoning & Agent Data Engineering (Ch18-Ch20)
├── Part 7: Application-Level Data Engineering (Ch21-Ch23)
├── Part 8: DataOps & Platform Engineering (Ch24-Ch26)
├── Part 9: Data Assets, Data Products & Data Contracts (Ch27-Ch30)
├── Part 10: Agentic Data Engineering (Ch31-Ch35)
├── Part 11: Privacy, Compliance & Data Security (Ch36-Ch37)
├── Part 12: Specialized Datasets and Multimodal Data Engineering Practice (Ch38-Ch43)
├── Part 13: Open-source Model Data Recipes (Ch44-Ch48)
└── Part 14: Project Case Studies (P01-P15)

Key Highlights

Comprehensive Theory

• Data-Centric AI philosophy throughout
• Covers the full LLM data lifecycle: Pre-training → Fine-tuning → RLHF → RAG → DataOps
• In-depth coverage of Scaling Laws, data quality evaluation, multimodal alignment, privacy compliance, and more

Modern Tech Stack

Domain	Technologies
Distributed Computing	Ray Data, Spark, Dask
Data Storage	Parquet, WebDataset, Vector Databases (Milvus/Qdrant)
Text Processing	Trafilatura, KenLM, MinHash LSH, fastText Quality Scoring
Multimodal	CLIP, ColPali, img2dataset
Data Versioning	DVC, LakeFS, MLflow
Platform Observability	Great Expectations, Evidently AI, Apache Airflow
Privacy & Security	Federated Learning, Differential Privacy, Secure MPC

Rich Capstone Projects

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 Instruction	Bbox alignment + multi-image interleaving	Visual instruction dataset
Synthetic Math Textbook	Evol-Instruct + sandbox verification	PoT reasoning dataset
Financial Report RAG	ColPali + Qwen-VL	Multimodal QA system
CoT Reasoning + PRM	Process Reward Modeling	Reasoning process dataset
Agent Tool-Use Factory	Tool-call chains + trajectory annotation	Agent training dataset
DataOps Platform	Airflow + DVC + quality monitoring	Enterprise data ops system
Privacy Pipeline	Federated Learning + Differential Privacy	Compliant training pipeline
LLM Data Flywheel	Online feedback + continuous iteration	End-to-end closed-loop system
Mini-DeepSeek Reproduction	Multi-source mixing + deduplication + tokenizer	Open-source pre-training data recipe
R1 Reasoning Flywheel	Multi-sampling + verifier + rejection sampling	Reasoning data loop
Multimodal Instruction Factory	VLM generation + judge filtering + multilingual expansion	Multimodal SFT dataset
Video Generation Dataset	Shot detection + motion filtering + multi-frame captioning	T2V training data pipeline
DataAgent Semantic NL2SQL Assistant	Semantic layer + Text-to-SQL + permission audit	Enterprise query-agent case study

Local Development

Requirements

• Python 3.8+
• MkDocs Material
• mkdocs-static-i18n (i18n support)

Install & Preview

# 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/Japanese language switcher).

Build Static Site

mkdocs build

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

Project Structure

data_engineering_book/
├── docs/
│   ├── zh/                    # Chinese content
│   │   ├── index.md           # Chinese homepage
│   │   └── part1/ ~ part14/   # Chinese Springer mainline chapters
│   ├── en/                    # English content
│   ├── ja/                    # Japanese content
│   ├── images/                # Image assets (shared)
│   ├── stylesheets/           # Custom styles
│   └── javascripts/           # JavaScript (MathJax etc.)
├── .github/workflows/         # GitHub Actions CI/CD
├── images/                    # Project image assets
├── mkdocs.yml                 # MkDocs configuration
├── LICENSE                    # License
├── README.md                  # 中文说明
├── README_en.md               # English README (this file)
└── README_ja.md               # 日本語 README

Target Audience

• LLM R&D Engineers
• Data Engineers / MLOps / DataOps Engineers
• AI Product Managers (Technical)
• Researchers interested in LLM data pipelines

Laboratory Information:
National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China;
Multimedia Computing and Intelligent Robotics Research Center, Department of Automation, University of Science and Technology of China;
Joint Research Center for Multi-Modal Intelligent Agents, Department of Automation, University of Science and Technology of China

Contributing

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

License

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

Contact

• GitHub Issues: Submit an issue
• Read Online: https://datascale-ai.github.io/data_engineering_book/en/

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