Which storage solution is recommended for the Research division's semantic models and reports?

Data Lake Gen2 storage is recommended for the Research division's semantic models and reports because it is designed specifically for big data analytics and can handle a vast amount of data efficiently. It integrates well with various analytics tools and provides features such as hierarchical namespace, fine-grained access control, and support for both structured and unstructured data.

Using Data Lake Gen2 storage allows for scalability, which is crucial given the potentially large datasets involved in research. It also offers cost-effective storage solutions, as data can be tiered based on how frequently it is accessed. This is especially beneficial for models and reports that might not need constant retrieval but require high performance when they are accessed.

In contrast, while Microsoft Azure Blob Storage can handle unstructured data and is useful for large-scale storage, it lacks certain analytics capabilities that Data Lake Gen2 offers. A SQL Database is more suited for structured data with transactional requirements and wouldn't provide the same level of scalability and analytics function needed for semantic models and reports. Microsoft OneDrive is primarily a file storage and sharing platform, which is not optimized for the specific needs of analytical workloads and handling extensive datasets.