Data Engineering with Generative and Agentic AI on AWS is a practical and forward-looking guide to transforming modern data engineering with generative artificial intelligence and agentic AI. As enterprises generate increasing volumes of structured and unstructured data, organizations need smarter ways to build reliable data pipelines, improve data quality, automate repetitive workflows, and turn information into valuable business insights. This book explores how AI can become a powerful partner for modern data professionals working within the AWS cloud ecosystem.
The book focuses on the emerging concept of an AI-augmented data practice, showing how generative AI and intelligent AI agents can complement traditional data engineering skills. Rather than replacing the fundamentals of data engineering, AI can help engineers accelerate development, troubleshoot problems, document systems, generate code, improve workflows, and support complex analytical tasks.
Readers can explore how generative AI for data engineering can be applied throughout the data lifecycle. Topics may include data ingestion, transformation, data pipelines, SQL development, Python programming, data quality, metadata management, documentation, testing, orchestration, and analytics. By combining established engineering practices with AI-powered capabilities, teams can work toward faster development cycles and more efficient enterprise data operations.
A major focus is the role of agentic AI in data engineering. AI agents can be designed to reason through multi-step tasks, interact with tools, analyze information, and support automated workflows. In an enterprise data environment, these capabilities can help professionals investigate data issues, assist with pipeline operations, coordinate repetitive processes, and improve productivity while maintaining appropriate human oversight.
The AWS ecosystem provides a powerful foundation for building scalable cloud-based data solutions. This book is particularly relevant for professionals interested in AWS data engineering, cloud data platforms, generative AI on AWS, machine learning, analytics, data pipelines, and enterprise AI transformation. It encourages readers to consider how AI capabilities can be incorporated into existing data architectures and organizational workflows.
Whether you are a data engineer, cloud architect, analytics professional, developer, technology leader, or enterprise decision-maker, this book provides valuable perspectives on the changing role of data engineering. It can help readers understand how AI-assisted development may influence everyday technical work while highlighting the importance of governance, security, reliability, data quality, and responsible AI adoption.
As organizations move toward AI-driven operations, the ability to combine high-quality enterprise data with generative and agentic AI is becoming increasingly important. Data Engineering with Generative and Agentic AI on AWS offers a timely perspective on this transformation, helping readers think strategically about building scalable, automated, and AI-augmented data practices.
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