Building real-time analytics with Redpanda, Iceberg, and Tinybird

Combining Redpanda, Apache Iceberg, and Tinybird creates a robust architecture for real-time analytics applications by integrating event streaming with durable data storage and analytics capabilities. Redpanda serves as a modern, Kafka-compatible event streaming platform that allows efficient event sourcing and storage, while Iceberg provides a versioned, durable data lake with schema evolution capabilities. Tinybird offers seamless, high-performance analytics APIs that can process both historical data from Iceberg and real-time data from Redpanda, enabling low-latency applications. This architecture addresses the limitations of traditional analytics systems, such as the slow response times of data warehouses, the lack of historical data retention in Kafka, and the real-time limitations of data lakes, by offering a horizontally scalable, developer-friendly solution with simple local setup and cloud deployment options. The process includes setting up Redpanda and Tinybird locally, connecting them for real-time streaming, and deploying analytics APIs to production, significantly enhancing developer productivity and minimizing infrastructure overhead.