Data virtualization in a Lakehouse architecture is not applicable as it only supports data stored within the data lake, making it unsuitable for integrating external data sources.

Data virtualization involves the physical consolidation of all data sources into a single repository within the Lakehouse, ensuring data is always up-to-date but at a higher storage cost.

Data virtualization creates a virtual layer that abstracts the underlying data sources, enabling seamless access and integration without the need for physical data movement, thus reducing costs and improving efficiency.

Data virtualization is primarily used for data backup and recovery purposes in a Lakehouse architecture, with minimal impact on data access or integration capabilities.