Justin J. Leto | Data Engineering with Generative and Agentic AI on AWS. Building an AI-Augmented Data Practice for the Enterprise (2026) [PDF, EPUB]
Автор: Justin J. Leto
Издательство: Apress
ISBN: 979-8-8688-2199-8, 979-8868821981
Жанр: Management Information Systems, Database Storage & Design, Software Design & Engineering
Язык: Английский
Формат: PDF, EPUB
Качество: Изначально электронное (ebook)
Иллюстрации: Цветные и черно-белые
Описание:Unlock the future of cloud data engineering with generative and agentic AI on AWS.
This hands-on guide shows you how to build intelligent, responsive data platforms using cutting-edge AI capabilities and modern AWS services.
Learn to design next-generation data architectures—from data lakes and data mesh to scalable pipelines and real-time analytics. Discover how generative AI and agentic automation are transforming every aspect of enterprise data work: ingesting unstructured data, enabling semantic search with Retrieval-Augmented Generation (RAG), building autonomous data agents, and using natural language interfaces to turn business questions into instant insights.
Author Justin J. Leto, PE, MBA, PMP, is a Principal Solutions Architect at AWS with over 20 years of experience in data engineering and AI. He doesn't just teach today's techniques—he prepares you for the future disruptions reshaping the field. His book is essential reading for current and aspiring data engineers, data analysts, data architects, engineering managers, CTOs, CDOs, and data-focused entrepreneurs looking to gain an edge over the competition.
What You Will Learn:
Master the core principles and practices of data engineering to build a long, successful career in the field.
Accelerate your impact using AWS cloud services for scalable, modern data solutions.
Explore how the role of the modern data engineer is evolving to support generative and agentic AI use cases.
Develop a modern data strategy by working backwards from business goals to gain buy-in from CxO-level leadership.
Design and deploy modern data architectures—including data lakes, data mesh, and data marts—and understand when to use each.
Apply generative and agentic AI to enhance every stage of the data engineering lifecycle.
Evaluate emerging data and AI technologies using proven methodology to separate real value from hype.
Prepare for the future of data engineering powered by autonomous agents that scale enterprise impact.
Who this Book Is For:
Data engineers, analysts, architects, and tech leaders seeking practical guidance on AWS data engineering and generative AI, with or without prior cloud experience.
About the Author xxi
About the Technical Reviewer xxiii
Acknowledgments xxv
Foreword xxvii
Introduction xxix
Chapter 1: Introduction to Data Engineering with Generative and Agentic AI on AWS 1
Chapter 2: Data Security and Governance 43
Chapter 3: Data Lake Design with Apache Iceberg and S3 Tables 99
Chapter 4: Data Mesh Design with Amazon DataZone 155
Chapter 5: Big Data Processing and Transformation with AWS Glue and AI Agents 191
Chapter 6: Data Pipeline Orchestration and Observability 257
Chapter 7: Multimodal Data Extraction and Enrichment with Amazon Bedrock and AWS ML Services 297
Chapter 8: Retrieval-Augmented Generation (RAG) with S3 Vectors and Vector Databases 349
Chapter 9: Streaming and Real-Time Data Processing with Generative AI Enrichment 421
Chapter 10: Data Warehousing with Generative AI and Text-to-SQL Reporting with Amazon Redshift 441
Chapter 11: Generative Business Intelligence with Amazon Quick Suite 483
Chapter 12: Building AI Agents with Bedrock AgentCore, Strands Agents, and Model Context Protocol (MCP) 513
Index 577
Скриншоты:
Время раздачи: с 10 до 20 (минимум до появления первых 3-5 скачавших) 
