🎤 CFP is still open for Data Streaming Summit 2026! 📅 Oct 7–8 📍 San Francisco This year, #DSS becomes The Data Streaming Agent Infra Conference — bringing together engineers building the infrastructure behind real-time data and AI-native applications. We're looking for talks on: ⚡ Streaming systems at scale 🤖 Production agent platforms 🔍 Observability, governance & runtime infrastructure 🌉 Architectures connecting streaming and agent workloads Selected speakers receive a free conference pass and promotion across DSS channels. Submit your talk today! 👉 hubs.ly/Q04l2Gxh0 #DataStreaming #ApacheKafka #ApachePulsar #Flink #AIAgents #DSS26

Tired of building complex ingestion pipelines just to get your streaming data ready for analytics and AI? Join @streamnativeio and @starburstdata for a live webinar on June 25th to learn how to bridge the gap between real-time Kafka streams and Apache Iceberg tables! 🌊🧊 We will show you how to simplify your journey to actionable insights using an open, modern data architecture. What we’ll cover: 🔹 Pipeline-free Kafka to Iceberg ingestion 🔹 Schema governance best practices 🔹 Optimizing Iceberg tables via LakeOps 🔹 Scaling with open standards (Kafka, Iceberg, Trino) 🔗 Save your seat: hubs.ly/Q04kT0CT0 #DataStreaming #ApacheKafka #ApacheIceberg #Lakehouse #RealTimeAnalytics #AIInfrastructure

Last week Snowflake launched Datastream — Kafka-compatible streaming built directly into the platform. To me that's a strong signal: the industry now agrees streaming and the lakehouse belong together, especially for AI agents that need to act on fresh data. That convergence is exactly the shift we've been building toward. We call it - #Lakestream. AI agents can't act on yesterday's data. They need the latest events — seconds old — sitting next to the full history that gives them meaning, governed enough to trust. That's real-time context, and it's what the classic two-system setup — streaming on one side, the lakehouse on the other, pipelines in between — was never designed to deliver. A Lakestream unifies the two, end to end on open standards. You produce and consume the live stream through the Kafka API, and you query that same data as an open Apache Iceberg table from any engine. One stream, written once — a low-latency event stream and an open table at the same time. The log that backs the stream is the table, one copy serving the present and the past at once. The lakehouse merged the lake and the warehouse. The #Lakestream does it one step earlier in the data's life: it merges the stream and the lakehouse — and keeps both sides open. We wrote up the full thesis 👇 streamnative.io/blog/what-is…

BIG NEWS: Today we’re announcing our native integration with Snowflake Horizon Catalog! ❄️🚀 Real-time streaming is nothing without real-time governance. Now, you can extend Snowflake’s premier security and compliance perimeter straight to your Kafka streams. StreamNative Kafka Service (#UFK) #Snowflake Horizon Catalog delivers: ⚡️ Zero-ETL: Kafka topics automatically land as open Apache Iceberg tables via #Ursa. 🛡️ Automated Cataloging: Streaming metadata automatically registers with Snowflake Horizon. 🔐 Unified Governance: Instantly inherits Snowflake RBAC, data masking, and lineage. 💰 95% Infrasctructure Savings: Cut traditional Kafka broker overhead while keeping data fully governed. Keep your existing Kafka apps. Keep your Snowflake governance. Ditch the complex ETL pipelines. 👉 Read the official launch blog: hubs.ly/Q04kgmFb0 #Snowflake #Datastream #Lakestream #Kafka #Horizoncatalog