Opis
For years, data platforms were built for analysts reading dashboards and ML models consuming flat features. To power AI, organizations must shift from passive dashboards to autonomous, real-time decisions. AI agents need live context, multi-modal data, governed knowledge, and trusted system access. Traditional ETL creates lag, while fragmented platforms stop safe reasoning.Architecting Agent-Ready Data Platforms gives you a blueprint for evolving legacy infrastructure into an active, agent-ready platform. You will learn to unlock enterprise knowledge by breaking silos and enabling agents to analyze text, documents, media, relational data, graphs, and operational signals in a multi-modal lakehouse. The book shows how to replace ambiguity with reliable AI responses by embedding business logic into knowledge graphs, metadata, and context layers that reduce hallucination.
You will explore modernizing batch pipelines into real-time flows, exposing governed data through secure context interfaces, and designing centralized agentic governance. By bringing AI models closer to trusted data and processing AI where the data lives, you can reduce cost, limit leakage, and support outcomes. By the end of the book, you'll be able to retrofit legacy stacks into secure, scalable, multi-modal platforms that power production-ready AI agents.