A decade after the cloud's replatforming of application infrastructure, agentic AI is initiating another profound shift focused on real-time data interaction. Today's AI landscape requires an adaptable data infrastructure catering to diverse roles including developers, machine learning engineers, and automated agents. Technologies like Apache Iceberg are central for facilitating this transition, providing a foundation for high-concurrency data access. The main challenge lies not only in implementing these systems but in managing ongoing operations effectively as businesses adjust to the rapid changes brought by intelligent systems.
Your biggest challenge? "Day two" operations. Building data infrastructure for agentic AI isn't just about implementation; it's about maintaining and evolving that infrastructure in response to ever-changing demands.
The AI data layer must serve polyglot, multi-persona teams. Traditional data platforms are no longer sufficient; today’s AI demands real-time access for a vastly expanded audience needing to work with data in various programming languages.
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