datascale-ai/data_engineering_book

What it solves

In the era of Large Language Models (LLMs), data quality is the primary constraint on model performance. Most teams currently lack a systematic approach to managing data, often relying on trial and error. This book provides a comprehensive technical framework to bridge that gap, covering the entire lifecycle of data engineering from raw collection to production deployment.

How it works

The book is structured into fourteen parts and 48 chapters, offering both theoretical foundations and practical implementation. It covers essential processes such as cleaning pre-training data, constructing instruction-tuning and preference datasets, building RAG pipelines, and managing DataOps. It includes 15 end-to-end project case studies with runnable code, architecture designs, and engineering reviews to help readers apply these methods to real-world scenarios.

Who it’s for

This resource is designed for LLM research engineers, data engineers, MLOps/DataOps professionals, technical AI product managers, and researchers interested in building robust data pipelines for large models.

Highlights

• Comprehensive coverage of the data lifecycle: pre-training, fine-tuning, RLHF, RAG, and DataOps.
• Modern technology stack integration including Ray, Spark, Dask, DVC, and various vector databases.
• 15 hands-on projects ranging from Mini-C4 pre-training sets to Agent tool-use and privacy-preserving pipelines.
• Focus on Data-Centric AI principles, including data quality assessment, multi-modal alignment, and privacy compliance.
• Open-source documentation available in Chinese, English, and Japanese, with support for local preview via MkDocs.