Moving data between operational systems and analytics platforms is often a painful process. Traditional pipelines that transfer data in and out of warehouses tend to become complex, brittle, and expensive to maintain over time.

Much of this complexity, however, is avoidable. Data in motion and data at rest—Kafka Topics and Iceberg Tables—can be treated as two sides of the same coin. By establishing an equivalence between Topics and Tables, it’s possible to transparently map between them and rethink how pipelines are built.

This talk introduces a declarative approach to bridging streaming and table-based systems. By shifting complexity into the data layer, we can decompose complex, imperative pipelines into simpler, more reliable workflows

We’ll explore the design principles behind this approach, including schema mapping and evolution between Kafka and Iceberg, and how to build a system that can continuously materialize and optimize hundreds of thousands of topics as Iceberg tables.

Whether you're building new pipelines or modernizing legacy systems, this session will provide practical patterns and strategies for creating resilient, scalable, and future-proof data architectures.