A lakehouse moves an organisation’s raw and structured data into one physical, cloud-hosted repository, rather than connecting existing systems in place or distributing ownership across teams. Everything lives in one system, under one team’s control.
That centralisation is also its main selling point: nothing beats it for the performance of large analytical queries, and having a single place to control access and quality is simpler to reason about than either a fabric or a mesh. It’s the slowest and most expensive of the three main paths to fixing fragmented data, typically twelve to eighteen months, and a poorly governed lakehouse just recreates the old bottleneck in a new location: one team, now controlling everything, instead of nothing.
It suits organisations that need strong analytical performance and are willing to invest the time and cost of a full migration to get it, rather than those optimising for speed to a working AI pilot.