Since the 1990s, operational and analytical data estates have been managed separately, traditionally connected through fragile ETL/ELT pipelines. Apache Kafka has emerged as a pivotal bridge between these estates, using its log-based architecture to facilitate seamless data integration and communication. Unlike point-to-point ETL, Kafka offers a centralized, scalable gateway that supports data streaming and change data capture without requiring numerous direct connections. Zero ETL, introduced by AWS, offers an alternative that simplifies data access by allowing users to query data in its original form, but it can struggle with scalability and tight coupling between data producers and consumers. Tableflow enhances Kafka's capabilities by leveraging open table formats like Apache Iceberg and Delta Lake to materialize Kafka topics into tables, enabling both low-latency access and table-oriented analytics. While zero ETL and Tableflow can be seen as competing solutions, they are complementary when integrated, with Tableflow providing scalability and schema transformation while maintaining Kafka's real-time data transfer strengths.