🌊 Data in Motion: Streaming & CDC for AI & Agents
When: 🕠 5:30–8:00 PM (🍽️ dinner included)
⚡ Real-time data is the foundation of modern analytics — and the fuel for AI and agent workflows. Agents can only learn, act, and adapt as fast as the data reaching them, which makes the streaming stack underneath matter more than ever.
Join VeloDB, Redpanda, and Datastrato for an evening on how teams are building that stack: 📥 ingesting at scale, 🔄 moving data with low latency, and 🔍 querying it the moment it lands — so your analytics, applications, and agents are always working from the freshest context available.
Three short talks, 🍕 dinner, and 🍻 conversation in SF!
✨ Speakers
Real-Time Data Architecture for the Agentic Era
🎤 Peter Corless — Principal Product Marketing Manager, Redpanda
Discover how enterprises are building enterprise-scale agentic AI applications. These real-time responsive systems require several components in their data architecture: event-driven data streaming, real-time analytics engines, and AI-centric application frameworks for governance, trust, and explainability.
Closing the Governance Gap Between the Stream and the Lakehouse
🎤Mark Hoerth — Product Lead & Solutions Architect, Datastrato
Streaming and analytics keep converging, but governance usually doesn't follow the data across the seam. A topic lives in one world with its own access model; the table it becomes lives in another. This talk shows how an open catalog can span both. Using Redpanda's broker-native Iceberg Topics to turn a live stream into an Apache Iceberg table with no ETL, and Apache Gravitino as the catalog of catalogs holding the topic and resulting table in a single metalake, we'll govern data the same way before and after it lands. The session ends with a live walk from produced records to a governed, queryable Iceberg table under one consistent policy. The takeaway: your governance boundary doesn't have to break where your streaming engine hands off to your lakehouse.Fast In, Fast Out: A Real-Time Analytics Stack with Apache Iggy (Incubating) and Apache Doris
🎤 Kranti, Founder and CEO, LaserData & Kevin Shen, PPM, VeloDB
This talk pairs two open-source projects to build a real-time analytics stack covering both fast ingest and fast queries: Apache Iggy (incubating), LaserData's Rust message-streaming platform that moves high event volumes at very low latency, and Apache Doris (the database behind VeloDB), which serves sub-second queries on fresh data under heavy concurrency, updates, and joins. After a brief intro to each project, the session walks through a Rust-native Iggy sink connector that streams events directly into Doris with no JVM or intermediary systems, then closes with a live demo pushing a real workload through Iggy into Doris and running analytics as the data lands—leaving attendees with an understanding of why real-time analytics needs both halves, how the connector wires them together, and when and how to build the stack themselves.⚠️ Important: Building Access Required
This event is hosted at the AWS office. You must register at both links to attend:
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✅ RSVP here on Luma
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✅ Register for building access: AWS Event Registration
