Which Azure service enables the storage and management of structured and unstructured data?

Azure Data Lake Storage is designed specifically for storing and managing both structured and unstructured data, making it ideal for handling vast amounts of data from various sources. It provides a scalable and secure environment that accommodates the needs of big data analytics workloads. The service allows users to store data in its native format, making it suitable for diverse scenarios, including data lakes where both types of data can co-exist efficiently.

This service is built on top of Azure Blob Storage and introduces features like hierarchical namespace and fine-grained access controls. These features enhance the management and organization of data, ensuring that users can leverage both structured data (like that found in a traditional relational database) and unstructured data (such as logs, images, or videos) effectively within their analytics workflows.

The other services mentioned focus on more specific use cases. Azure SQL Database, for instance, primarily deals with structured data in a relational database format. Azure Cosmos DB supports multiple data models, including semi-structured data, but is not specifically optimized for unstructured data at the same level as Azure Data Lake Storage. Azure Data Factory is a data integration service that allows for ETL (extract, transform, load) processes but does not serve as a primary storage solution.