Data lakehouse
A data platform architecture that combines the low-cost, flexible storage of a data lake with warehouse-style features such as transactions, schema enforcement and fast analytical queries. It aims to serve reporting, data science and AI from one platform.
Why it matters
Running a separate lake and warehouse means copying data between them, duplicating governance and reconciling differences. A lakehouse can reduce that duplication and keep raw, refined and modelled data in one governed environment.
It is an architecture pattern rather than a guarantee. The benefits depend on disciplined data modelling, access control and cost management.
In practice
For example, a UK e-commerce group might consolidate clickstream data, order data and product images into a lakehouse, with refined tables for finance reporting and raw data available to its data science team under the same permissions model.
Where Rodan fits
Rodan designs and builds lakehouse platforms through Data and Analytics Engineering and Platform and Cloud Engineering.