Data Engineering for Large Foundation Models: A Handbook¶

Full Table of Contents Overview¶

The current Chinese mainline uses the 2026 Springer-size publication structure. The main text covers 48 chapters, 15 end-to-end projects, and 8 appendices (A-H). To reduce friction when reading across parts, this edition adds online resource entry points, a unified abbreviation table in the front matter, and a contents page for each part.

Part-by-Part Contents¶

Part 1: Overview and Infrastructure¶

This part establishes the core framework for large-model data engineering: how the data lifecycle, quality evaluation, AI-native data stack, and cost governance fit together.

Part 2: Text Pre-training Data Engineering¶

This part focuses on large-scale text corpora, including data sources, acquisition and copyright, cleaning, deduplication, decontamination, tokenization, serialization, efficient loading, and quality operations.

Part 3: Multimodal Data Engineering¶

This part covers image-text, document, video, audio, and cross-modal alignment data, with attention to sample structure, quality control, annotation augmentation, and fusion training.

Part 4: Instruction Fine-tuning and Preference Data¶

This part centers on model alignment data, covering SFT instruction systems, preference data, reward signals, annotation platforms, QA, and data operations.

Part 5: Synthetic Data Engineering¶

This part explains the path from seed samples to a synthetic data factory, including knowledge distillation, model collaboration, quality control, and model-collapse risks.

Part 6: Reasoning and Agent Data Engineering¶

This part covers chain-of-thought data, reasoning traces, tool use, function calling, agent memory, and multi-turn interaction data.

Part 7: Application-Level Data Engineering¶

This part targets RAG and online knowledge systems, including document parsing, visual retrieval, multimodal RAG, online feedback loops, and knowledge updates.

Part 8: Data Operations and Platform Development¶

This part builds sustainable data platform capabilities through team organization, version management, experiment tracking, and observability.

Part 9: Data Assets, Data Products, and Data Contracts¶

This part turns data pipelines into discoverable, reusable, auditable organizational assets through catalogs, metadata governance, data products, contracts, valuation, reuse, and internal data markets.

Part 10: Intelligent Data Engineering and Data Engineering Agents¶

This part discusses how data engineering agents participate in acquisition, parsing, cleaning, annotation, synthesis, evaluation, DataOps, security, permissions, and human-AI collaboration.

Part 11: Privacy Compliance and Data Security¶

This part focuses on compliance frameworks, privacy protection, federated learning, security boundaries, and auditable controls across the data lifecycle.

Part 12: Specialized Datasets and Multimodal Data Engineering Practice¶

Part 12 follows a modality-explicit path across text corpora, image-text candidate pools, visual documents and tables, visual reasoning, speech and audio, and reasoning traces. It explains how specialized datasets are defined, constructed, evaluated, released, governed, and reproduced, while connecting project case studies with open-source model data recipes.

Part 13: Open-source LLM Data Engineering Recipes and Paradigms¶

This part focuses on data recipes, training paradigms, and engineering organization for open-source large models, covering pre-training, post-training, reasoning RL, VLMs, and T2I/T2V generation.

Part 14: Practical Projects¶

This part connects acquisition, cleaning, synthesis, RAG, agents, DataOps, privacy, data flywheels, open-source model reproduction, video-generation data pipelines, and enterprise semantic data agents into runnable projects.