Generative AI rapidly transforms data architectures, reshaping how organizations govern, manage, and analyze data. As organizations face growing demands for real-time insights, scalability, and compliance, traditional data systems struggle to keep pace. Generative AI-powered data architectures offer a shift, enabling intelligent data governance, automated management, and analytics that evolve autonomously. These architectures streamline complex data operations while enhancing decision-making through proactive, AI-driven insights. Further exploration of these infrastructures may reveal more adaptive, explainable, and capable data to support new innovations in enterprise analytics. Generative AI-Powered Data Architectures: From Governance to Autonomous Analytics explores how generative AI can be effectively integrated with modern data architectures to build scalable, secure, and intelligent systems. It addresses the entire data lifecycle from governance and ingestion to transformation, storage, and autonomous analytics empowering organizations to leverage AI-driven processes. This book covers topics such as data analytics, learning systems, and ethics and law, and is a useful resource for engineers, educators, business owners, academicians, researchers, and data scientists.