WarpStream has introduced Tableflow, a solution for efficiently converting Kafka topic data into Iceberg tables with low latency. The article discusses the challenges of using Spark for this process, including high latency, small file issues, and the complexity of managing data lakes. It ultimately argues that relying on Kafka's tiered storage for building Iceberg tables is impractical due to various performance issues encountered in real-world scenarios.

The concept of "zero-copy" integration between Apache Kafka and Apache Iceberg, which suggests that Kafka topics could directly function as Iceberg tables, is critiqued for its inefficiencies and potential pitfalls. The article argues that while it may seem to offer reduced duplication and storage costs, it actually imposes significant compute overhead on Kafka brokers and complicates data layout for analytics. Additionally, it highlights challenges related to schema evolution and performance optimization for both streaming and analytics workloads.

To transfer data from Apache Kafka to Apache Iceberg, various options exist, including Apache Flink SQL, Kafka Connect, and Confluent's Tableflow. Each method has its own strengths and considerations, such as data structure, existing deployment preferences, and the number of Kafka topics involved, guiding users in selecting the most suitable solution for their specific use case.