AI Data Engineering

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.