Businesses leveraging streaming data platforms like Redpanda need offline capabilities to analyze historical data while maintaining real-time operational insights, typically achieved through data lakehouse architectures using tools like Apache Spark, Snowflake, and Databricks. The upcoming integration of Apache Iceberg in Redpanda simplifies accessing streaming data as Iceberg tables, which originated at Netflix and are now standard for creating scalable data lakes. Iceberg's table format stores metadata about data files, ensuring efficient queries, schema evolution, and interoperability with various analytics tools, thereby simplifying the management and accessibility of large datasets. Redpanda's Iceberg integration allows data to be stored from Redpanda in Iceberg without extra configurations, enabling seamless data flow into data lakes and easy querying with SQL on platforms like ClickHouse. This integration eliminates the need for complex data engineering jobs or configuration-heavy systems like Kafka Connect, making data management more efficient and accessible for analysts and data scientists while maintaining real-time streaming capabilities.