Build the Data Platforms Behind the Next Generation of Artificial Intelligence.
Artificial intelligence is only as powerful as the data infrastructure supporting it. Modern AI applications require more than traditional ETL pipelines. They need reliable data platforms, scalable processing systems, intelligent retrieval architectures, high-quality datasets, and production-ready AI workflows.
AI Data Engineering provides a practical and comprehensive guide to designing, building, and operating modern data platforms for AI and enterprise applications.
From foundational data engineering concepts to advanced generative AI architectures, this book connects traditional data platforms with the technologies powering today's intelligent systems.
WHAT YOU WILL LEARN
Python, SQL, databases, data modeling, and data architecture
ETL and ELT pipelines, Azure Data Factory, and workflow orchestration
Apache Spark, PySpark, Databricks, and lakehouse architectures
Batch processing, streaming systems, and incremental data pipelines
Data quality, governance, metadata, lineage, and security
Large Language Models (LLMs), tokens, prompts, and embeddings
Vector databases, semantic search, and similarity retrieval
Retrieval-Augmented Generation (RAG) and advanced retrieval techniques
LangChain, LangGraph, AI agents, and Model Context Protocol (MCP)
AI application development, APIs, FastAPI, and deployment
Docker, Kubernetes, CI/CD, monitoring, and production operations
AI evaluation, observability, guardrails, privacy, and cost optimization
EXPLORE PRACTICAL ARCHITECTURES AND PROJECTS
Learn how data moves from enterprise sources through ingestion, transformation, lakehouse storage, document processing, embedding generation, vector search, retrieval systems, and AI applications.
Understand how to design reliable pipelines, build retrieval systems, integrate AI services, deploy applications, and monitor production workflows.
WHO SHOULD READ THIS BOOK?
Data Engineers and aspiring Data Engineers
AI Engineers and Machine Learning Engineers
Cloud and Big Data Professionals
Software Engineers transitioning into AI
Computer Science students and technology learners
Professionals building enterprise AI and GenAI systems
Whether you are strengthening your data engineering foundation or moving toward AI engineering, this book provides a structured learning path for understanding the infrastructure, architectures, tools, and engineering practices behind production-ready AI systems.
AI does not replace data engineering. It makes strong data engineering even more important.
