For years, enterprises invested heavily in decoupled analytics stacks—separating underlying storage from complex, third-party compute clusters. While this approach offered initial flexibility, it has introduced a steep operational debt that must be paid. Highly skilled data engineers spend their working hours sizing compute clusters, managing idle capacity, and building fragile pipelines to shuffle data across security boundaries. Autonomous AI agents cannot reason effectively over stale batch pipelines, and moving massive datasets to external processing layers inflates cloud spend while compromising data governance.