Data warehouses differ from traditional databases primarily in their purpose and design. While traditional databases are optimized for transactional processing (OLTP), data warehouses are built for analytical processing (OLAP), focusing on the storage and analysis of large volumes of historical data. Data warehouses support complex queries, aggregations, and data mining, allowing organizations to derive insights through business intelligence. Additionally, data warehouses utilize a schema design such as star or snowflake schemas, which facilitates an organized structure for analysis, while traditional databases typically follow a normalized schema to minimize redundancy.