How does a Data Warehouse differ from a Data Lake?

The distinction between a Data Warehouse and a Data Lake lies primarily in the type of data each is designed to handle and their respective purposes. A Data Warehouse is specifically built to store structured data that has been processed and organized in a way that is optimized for query and analysis. This structure allows for efficient retrieval and reporting, making it ideal for business intelligence applications where users require consistent data formats to run complex queries and generate insights.

In contrast, a Data Lake is designed to accommodate a vast array of data types, including unstructured and semi-structured data, which may not have a pre-defined schema. This flexibility allows organizations to store raw data in its native format, making it possible to retain a comprehensive repository that can be analyzed at a later time. However, it is the role of the Data Warehouse that stands out for analytics; it transforms and structures data to ensure high performance and ease of access for analytical purposes.

Hence, the correct answer reflects the primary function of a Data Warehouse in contrast to a Data Lake, clarifying its role in managing and optimizing structured data for analytical tasks.