The integration of Apache Kafka with Apache Iceberg offers an innovative solution for bridging the gap between real-time data streaming and analytical workloads, eliminating the need for complex ETL pipelines. Apache Iceberg, a table format optimized for large-scale analytic datasets, provides features such as ACID transactions, schema evolution, and time travel, making it suitable for data lakes. Iceberg Topics allow Kafka to write data directly into Iceberg table format, facilitating seamless querying through engines like Spark or Trino while maintaining Kafka API compatibility. This integration simplifies architectures by reducing duplicate storage systems and infrastructure costs, enabling immediate real-time analytics without batch processing delays. With the help of Aiven's Remote Storage Manager plugin, users can experiment locally using Docker to set up a complete environment, demonstrating how Kafka streams data into Iceberg tables stored in MinIO. The synergy between Kafka and Iceberg heralds a shift toward unified streaming and analytical systems, offering significant benefits for stream processing, data engineering, and analytics teams by reducing operational complexity and enhancing data accessibility.