Master AI-based data engineering using skills.md, context engineering, agentic pipelines, and enterprise AI architecture to transform traditional data teams into AI-native platform builders
Key Features
- Design skill-driven data systems using skills.md and AI agents
- Replace scripts and DAGs with autonomous, context-aware pipelines
- Architect autonomous, scalable, and governed AI-native data platforms
Book Description
Data engineering is undergoing a structural transformation. As AI systems become embedded in enterprise workflows, traditional pipelines, manual scripts, and static orchestration frameworks can no longer keep pace. The future belongs to AI-native data platforms built on context engineering, skill registries, and autonomous agents. AI-Based Data Engineering introduces a new paradigm where Skills > Scripts. Instead of hard-coded ETL logic, you will design reusable, modular skills defined in skills.md that agents can dynamically load, reason about, and execute. You will learn how to build context graphs that unify metadata, lineage, documentation, semantic layers, and business knowledge into machine-readable intelligence. The book explores the integration of large language models (LLMs) with modern data platforms, vector databases, semantic layers, and metadata systems. It also covers AI governance, data-quality automation, real-time reasoning, and performance-optimization strategies for cloud data architectures. By the end of this book, you will be able to design AI-powered data engineering systems that move beyond static ETL toward adaptive, self-healing, and intelligent data platforms capable of supporting advanced analytics, AI applications, and enterprise-scale decision intelligence.
What you will learn
- Understand the shift from scripts to skill-driven systems
- Design and implement skills.md registries
- Build enterprise context graphs for AI reasoning
- Integrate LLMs with structured and unstructured data
- Automate pipeline creation and validation using AI
- Implement AI governance and compliance controls
- Design agent task routing across data platforms
- Transform data teams into AI-native organizations
- Optimize performance with semantic and vector layers
Who this book is for
This book is designed for data engineers, data architects, AI engineers, machine learning practitioners, and technical leaders modernizing enterprise data platforms. If you are building AI-ready data infrastructure, integrating LLMs into production systems, or transforming traditional ETL pipelines into intelligent data architectures, this book provides the architectural patterns and implementation strategies you need
