Confluent Tableflow integrates with Trino to enable seamless integration of streaming and batch data using Apache Iceberg tables. This allows users to query and visualize Apache Iceberg tables effortlessly in Jupyter Notebooks, making it easier to derive insights from their Confluent Cloud data. With Tableflow, organizations can persist and structure streaming data into Iceberg tables stored in cloud object storage, ensuring efficient, scalable, and cost-effective analytics. The integration supports various query engines and data catalogs, including Trino, Apache Spark, DuckDB, and Amazon SageMaker Lakehouse. By enabling users to work with real-time data and aggregate insights like total net sales per user, Tableflow provides a powerful solution for businesses struggling to bridge the gap between real-time event streaming and analytical workloads.