Data Engineering with Generative and Agentic AI on AWS
Description
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.
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Person
Justin J. Leto , PE, MBA, PMP, is a Principal Solutions Architect at AWS with over 20 years of experience leading data engineering, machine learning, and AI initiatives for enterprise transformation. A recognized thought leader, he has delivered keynote presentations at premier conferences including Spark+AI Summit and AWS re:Invent, and has contributed to more than 10 technical AWS blogs. He leads the NYC Generative and Agentic AI Meetup, which has over 12,000 members. Justin specializes in helping organizations modernize their operations through cloud-native data strategies and agentic automation to drive measurable business outcomes. He is a licensed Professional Engineer and holds an MBA and a B.S. in Computer Engineering from The Pennsylvania State University.
Content
Chapter 1: Introduction to Data Engineering with Generative and Agentic AI on AWS.- Chapter 2: Data Security and Governance.- Chapter 3: Data Lake Design with Apache Iceberg and S3 Tables.- Chapter 4: Data Mesh Design with Amazon DataZone.- Chapter 5: Big Data Processing and Transformation with AWS Glue and AI Agents.- Chapter 6: Data Pipeline Orchestration and Observability.- Chapter 7: Data Extraction and Enrichment with Generative AI and ML Services.- Chapter 8: Retrieval-Augmented Generation (RAG) with S3 Vectors and Vector Databases.- Chapter 9: Streaming and Real-Time Data Processing.- Chapter 10: Data Warehousing and Text-to-SQL Reporting with Amazon Redshift.- Chapter 11: Generative Business Intelligence with Amazon QuickSight Q.- Chapter 12: Building AI Agents with Bedrock AgentCore, Strands Agents, and Model Context Protocol (MCP).
