Real-time data streaming is crucial for modern businesses, but existing infrastructure often struggles to support the necessary real-time analytics and historical context analysis. Apache Iceberg, an open-source table format initially developed by Netflix, addresses these challenges by organizing large analytic datasets into clearly defined schemas with rich metadata, enabling scalable and reliable data processing. Iceberg tables bring database-like functionality to data lakes, ensuring consistency across batch and streaming pipelines, and are compatible with various analytics engines such as Apache Spark, Snowflake, and Amazon Redshift. They simplify data operations by providing features like ACID compliance, schema evolution, time travel, and efficient data pruning, all of which enhance performance while reducing latency and improving resource utilization. Iceberg's architecture, which includes catalog, metadata, and data layers, makes it easier to manage changes and track data history, thereby solving common issues associated with streaming data into data lakes, such as latency, data quality challenges, and scalability limitations. With tools like Redpanda, streaming data can now be directly integrated into Iceberg tables, offering a streamlined approach to unifying batch and streaming analytics without complex ETL workflows.