What defines a "Data Lakehouse"?

A "Data Lakehouse" is defined as a hybrid model that combines features of both data lakes and data warehouses. This concept allows organizations to leverage the benefits of both storage architectures in a unified system. Data lakes are designed to handle large volumes of raw, unstructured data, enabling organizations to store data in its original format. Meanwhile, data warehouses are optimized for structured data and are used for analytics and business intelligence.

In a Data Lakehouse, the flexibility and scalability of data lakes are combined with the performance and management features characteristic of data warehouses. This means users can store vast amounts of diverse data in one place and perform analytics on it seamlessly. This hybrid approach caters to various data processing requirements, making it an attractive option for modern data architectures.

The other options highlight characteristics that do not encapsulate the full concept of a Data Lakehouse. For instance, a storage solution exclusively for unstructured data would limit its utility because Data Lakehouses also manage structured data. A cloud-only solution for big data processing ignores the flexibility of being able to implement Data Lakehouses on-premises or in hybrid cloud setups. Lastly, describing it as an on-premises database management system overlooks the essential attributes of combining data lake and warehouse functions, as well as the cloud-native