Kundan Vyas

Fresh off the keynote? Join us for an interactive follow-up discussion and open Q&A on the Streamhouse framework. We'll dive deeper into how this open, vendor-neutral data architecture keeps real-time context continuously available for modern applications and AI agents.

An agent is only as good as its context — and most context today is stale. Documents, vector indexes, and lakehouse tables all describe the world as it was when they were last written. The freshest, highest-value context in an enterprise is in motion: it lives in the streams. But raw Kafka topics aren't usable as agent context — there's no governance, no schema contract, no identity or audit, and no way to discover or query a topic as a dataset. The Lakestream Catalog changes that. It makes streams first-class, governed catalog objects — discoverable, schema-contracted, access-controlled, and auditable — explicitly linked to the Iceberg/Delta tables they materialize into, and federated with the catalogs you already run (i.e Unity Catalog, Iceberg REST). We'll cover the core model and the streaming semantics that table catalogs don't capture, then show how the catalog becomes the governance plane that lets agents reach live data through one secure interface. The payoff: governed real-time context for agents, on the streams you already have. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

Modern applications generate an endless stream of events, but users expect to analyze both what happened seconds ago and what happened years ago—through a single, low-latency interface.

Real-time data powers modern AI and analytics yet governing high-velocity streaming data in the lakehouse remains a challenge. In this session, StreamNative and Databricks showcase a native integration that brings governance to streaming pipelines through Databricks Unity Catalog without sacrificing performance or simplicity. We introduce catalog-based commits, a new approach where streaming data written in Delta Lake and Apache Iceberg formats is registered and governed in Unity Catalog at commit time. This eliminates post-processing, manual metadata synchronization and governance gaps, while enabling consistent access control, lineage and auditing across streaming and batch workloads. Attendees will learn how this integration reduces operational complexity, accelerates AI and real-time analytics and delivers a seamless path from event streams to governed, AI-ready lakehouse tables.

Two forces are reshaping Apache Kafka: diskless architectures and Apache Iceberg as the lakehouse standard. Most solutions choose one; few unify both natively. This session introduces Lakestream—an architecture combining diskless streaming with Iceberg into a single storage layer—and Ursa for Kafka (UFK) as proof that native Kafka can run directly on it. We'll walk through: Why diskless alone falls short without native lakehouse integration How Lakestream separates compute and storage while writing Kafka topics as queryable Iceberg tables (no pipelines) Flexible cluster profiles to optimize for latency or cost, including per-topic control Seamless migration from existing Kafka deployments without application changes Eliminating Kafka-to-lakehouse ETL by maintaining a single source of truth You’ll leave with a clear view of a diskless, Iceberg-native Kafka—and how to get there. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.