What Is LTAP in the Lakehouse?

This article examines LTAP (Lakehouse Transactional Analytical Processing) in the context of data architecture, emphasizing the need for clear contracts around write frequency, snapshot visibility, freshness windows, and workload boundaries. It critiques the simplistic question of whether tables can be updated, instead focusing on how fresh writes and analytical reads can coexist without confusion, especially as AI agents accelerate decision cycles. The article references Apache Iceberg, Flink, and Kafka specifications to ground the discussion, and outlines a five-layer architecture pattern. It includes practical guidance on common failure modes, guardrails for agentic use, and operational checklists for engineers, data owners, and executives.