B. Data virtualization Data virtualization is the best option among the choices for integrating multiple data warehouses while reducing the workload required for moving data between them. Data virtualization allows you to create a logical layer that abstracts the physical locations and formats of data, making it easier to access and query data from multiple sources without the need for extensive data movement or transformation. This approach can significantly reduce the workload and complexity associated with traditional Extract, Transform, and Load (ETL) processes, real-time data mirroring, or streaming data integration.

Data virtualization would best integrate multiple data warehouses while reducing the workload required for moving data between the warehouses. Data virtualization is an approach that allows organizations to access and query data from multiple sources, including different data warehouses, without physically moving the data. It provides a logical layer that abstracts the underlying physical data storage, enabling users to query and analyze data as if it were stored in a single location. This approach helps in avoiding the need for extensive data movement, reducing the workload associated with extracting, transforming, and loading (ETL) data between warehouses. It promotes a more agile and efficient data integration strategy, allowing organizations to access and analyze data across multiple sources in real-time.

Data virtualization is the technology that integrates information from different data sources and makes it directly accessible to users and applications. This approach allows data to be retrieved and used in real time without physically moving or duplicating the data.