Lakshmikant Shrinivas

Real-time analytics is critical for modern businesses — but bridging the gap between fast-moving Kafka streams and query-ready lakehouse tables remains a complex challenge. At Starburst, we encountered this firsthand while ingesting our internal telemetry data into Iceberg tables. Existing solutions fell short, plagued by issues such as lack of exactly-once guarantees, limited scalability, head-of-line blocking, small file proliferation, and high operational overhead and cost. This prompted us to rethink streaming ingestion from the ground up. Our goal was a fully managed, highly available system that makes data actionable within minutes, optimized for both performance and usability. We designed and built a custom Kafka-to-Iceberg ingestion service that directly writes data in Iceberg format with strong guarantees, minimal latency and continuous data maintenance for optimal query performance. Along the way, we developed novel techniques—such as Iceberg-aware commit coordination and adaptive Kafka consumer assignment—to overcome typical ingestion bottlenecks, as well as offer best-in-class price/performance. In this talk, we’ll walk through our architecture, the key design…