Moving data between operational systems and analytics platforms is often painful. Traditional pipelines become complex, brittle, and expensive to maintain.Take Kafka and Iceberg: batching on Kafka causes ingestion bottlenecks, while streaming-style writes to Iceberg create too many small Parquet files—cluttering metadata, degrading queries, and increasing maintenance overhead. Frequent updates further strain background table operations, causing retries—even before dealing with schema evolution. But much of this complexity is avoidable. What if Kafka Topics and Iceberg Tables were treated as two sides of the same coin? By establishing a transparent equivalence, we can rethink pipeline design entirely. This session introduces Tableflow—a new approach to bridging streaming and table-based systems. It shifts complexity away from pipelines and into a unified layer, enabling simpler, declarative workflows. We’ll cover schema evolution, compaction, topic-to-table mapping, and how to continuously materialize and optimize thousands of topics as Iceberg tables. Whether modernizing or starting fresh, you’ll leave with practical insights for building resilient, scalable, and future-proof data architectures.