A lakehouse moves an organisation’s raw and structured data into one physical, cloud-hosted repository. 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.