Data Engineering with Generative and Agentic AI on AWS

Data Engineering with Generative and Agentic AI on AWS is a vital resource for professionals seeking to enhance their data practices using cutting-edge AI technologies. This book delves into essential topics such as model context protocol, data mesh implementation, and safeguarding with Amazon Bedrock, equipping readers with actionable insights. With a focus on practical application, it addresses challenges like disaster recovery and data quality, making it indispensable for data engineers working with AWS.

What You'll Learn

• Implement data mesh using Amazon DataZone for scalable architectures
• Apply AWS IAM for secure identity and access management
• Enhance data pipeline orchestration with best practices
• Leverage schema-on-write vs. schema-on-read in data strategies
• Conduct data profiling and analysis to ensure quality

Who This Book Is For

Data engineers and IT professionals interested in applying generative and agentic AI to enterprise data practices will find this book invaluable. It assumes familiarity with AWS and basic data engineering concepts.

What You Need to Know First

Readers should have a basic understanding of AWS services and data engineering principles. Prior experience with cloud platforms will be beneficial.

Why This Book

Unlike other texts, this book integrates generative AI with AWS tools, offering a comprehensive guide to transforming enterprise data practices. Its unique focus on real-world application sets it apart.

What's Inside

Authored by Justin J. Leto and published by Apress, this 619-page volume includes detailed chapters on topics like Apache Iceberg, semantic search, and data scaling challenges. It offers both theoretical concepts and practical case studies.