What's the difference between a data lakehouse and a data warehouse?

A data warehouse is a structured repository optimized for storing and analyzing processed, curated data, typically in a relational format. It's designed for business intelligence and reporting, ensuring data quality and consistency. In contrast, a data lake is a vast, raw repository that stores data in its native format, structured or unstructured. It's highly flexible and cost-effective for storing large volumes of diverse data, but requires significant effort to process and prepare data before analysis. A data lakehouse aims to combine the benefits of both, offering the flexibility and scalability of a data lake with the structure, governance, and performance of a data warehouse, often by adding a metadata and governance layer on top of the data lake.