Feed your data lake with AI-ready tables for 30-50% lower cost

What this post added

Introduces Tableflow, a new capability that directly converts Apache Kafka topics into analytics-ready tables in formats like Apache Iceberg and Delta Lake. This feature aims to reduce data ingestion costs by 30-50% compared to traditional ETL pipelines. It achieves this by reusing Kafka segments stored in Confluent Cloud's Kora engine, converting them to Parquet, automatically handling schema evolution and CDC, compacting small files, and publishing tables to external catalogs. The post details the cost drivers of traditional ingestion stacks and highlights how Tableflow addresses them, including Kafka services and storage, connect infrastructure, lakehouse compute, and engineering overhead. It also provides benchmark data comparing Tableflow to managed lakehouse solutions and discusses architectural benefits like openness, operational simplicity, near-real-time data, and shift-left processing.