In this session, Paul Wilkinson, Principal Solutions Architect at Redpanda, will demonstrate Redpanda's native Iceberg capability: a game-changing addition that bridges the gap between real-time streaming and analytical workloads, eliminating the complexity of traditional data lake architectures while maintaining the performance and simplicity that Redpanda is known for.

Paul will explore how this new capability enables organizations to seamlessly transition streaming data into analytical formats without complex ETL pipelines or additional infrastructure overhead in a follow-along demo - allowing you to build your own streaming lakehouse and show it to your team!

Redpanda, a leading Kafka API-compatible streaming platform, now supports storing topics in Apache Iceberg, seamlessly fusing low-latency streaming with data lakehouses using BigQuery and BigLake in GCP. Iceberg Topics eliminate complex & inefficient ETL between streams and tables, making real-time data instantly accessible for analysis in BigQuery This push-button integration eliminates the need for costly connectors or custom pipelines, enabling both simple and sophisticated SQL queries across streams and other datasets. By combining Redpanda and Iceberg, GCP customers gain a secure, scalable, and cost-effective solution that transforms their agility while reducing infrastructure and human capital costs.

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