Build AI-powered data platforms that unify analytics and intelligence The playbook for data and AI architecture has been rewritten. This is the guide to what comes next. For decades, data architectures were optimized for a predictable set of workloads. AI changes every layer of the stack where autonomous agents, not just humans, consume data. The architectures that worked for traditional analytics cannot meet these demands. Designing the AI-Driven Data Foundations is a comprehensive guide to architecting platforms for this new reality. Drawing on his experience as principal analyst at SanjMo and former VP of Research at Gartner, where he advised thousands of enterprises on data strategy, renowned analyst Sanjeev Mohan delivers vendor-neutral guidance for navigating a landscape where yesterday's best practices no longer apply. The book systematically unpacks each layer of contemporary data stacks, from operational and analytical data stores through ingestion, integration, analytics, generative AI, governance, security, privacy, and operations.

The data architecture landscape is experiencing unprecedented disruption. AI has catalyzed changes across the entire data management stack: hardware optimized for vector operations, engineering practices reimagined for unstructured content, and consumption patterns transformed by autonomous agents. Designing the AI-Driven Data Foundations examines how the rise of unstructured data and AI agents as first-class consumers are redefining architectural principles that have guided the industry for decades. You'll also discover: A unified and comprehensive data and AI strategy Techniques to avoid vendor lock-in Frameworks for evaluating data stores Contextual data integration pattern that replaces ETL for AI workloads Coverage of AI-ready data governance, quality and security DataOps and observability practices that operationalize trust at scale Perfect for data architects, and technical leaders who must make consequential platform decisions, Designing the AI-Driven Data Foundation s translates the complexity of data and AI infrastructure into actionable architectural decisions that serve both human analysts and autonomous agents.