Sijie Guo

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.

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.

As data architectures evolve to meet the demands of real-time GenAI applications, organizations increasingly need systems that unify streaming and batch processing while maintaining compatibility with existing tools. The Ursa Engine offers a Kafka-API-compatible data streaming engine built on Lakehouse (Iceberg and Delta Lake). Designed to seamlessly integrate with data lakehouse architectures, Ursa extends your lakehouse capabilities by enabling streaming ingestion, transformation and processing — using a Kafka-compatible interface. In this session, we will explore how Ursa Engine augments your existing lakehouses with Kafka-compatible capabilities. Attendees will gain insights into Ursa Engine architecture and real-world use cases of Ursa Engine. Whether you're modernizing legacy systems or building cutting-edge AI-driven applications, discover how Ursa can help you unlock the full potential of your data.

Apache Pulsar is the cloud-native, messaging and streaming platform for modern companies. Integrating it with Flink could help solve the complex data storage and computing challenges in various enterprise scenarios.

As organizations getting better at capturing streaming data and the data velocity and volume are ever-increasing, the traditional messaging queues or log storage systems are suffering from scalability or operational and maintenance problems.

Both Apache Pulsar and Apache Flink share a similar view on how the data and the computation level of an application can be “streaming-first” with batch as a special case streaming.