Building AI-Ready Data Platforms: Architecture Patterns for Analytics, RAG, and AI Agents
About this session
Many organizations have modernized their analytics platforms, they often discover that the same infrastructure is not ready to support Retrieval-Augmented Generation (RAG), AI agents, and enterprise-scale generative AI applications. Data silos, inconsistent governance, poor metadata, and fragmented architectures can limit the accuracy, reliability, and scalability of AI solutions. In this session, we'll explore the essential architecture patterns for building AI-ready data platforms that support both traditional analytics and modern AI workloads. You'll learn how to design a unified data foundation using lakehouse principles, robust metadata management, semantic models, vector search, and governance to enable trusted AI experiences. Using real-world architecture examples and implementation best practices, we'll examine how data flows from ingestion and transformation through knowledge retrieval and AI applications. We'll also discuss common design trade-offs, including structured versus unstructured data, real-time versus batch processing, and balancing performance, security, and cost. Whether you're modernizing an existing data platform or planning an AI initiative, this session provides a practical roadmap for creating a scalable, secure, and future-ready data architecture that powers analytics, RAG, and AI agents.
Speaker
Key takeaways
- Understand the building blocks of an AI-ready data platform, including lakehouse architecture, metadata, semantic models, vector search, and governance
- Learn architecture patterns that support analytics, RAG, and AI agents from a unified and trusted data foundation.
- Explore design considerations for scalability, security, governance, and real-time data processing in enterprise AI solutions