SQL has traditionally been used for querying static, historical datasets, providing a snapshot of results at a specific moment. However, the emergence of real-time data processing needs has led to the development of Streaming SQL, which extends traditional SQL's capabilities to handle continuous data streams. Key differences include the ability of Streaming SQL to perform continuous queries with real-time updates, utilize window functions for segmenting data streams, and introduce watermarks to handle late-arriving data. Streaming SQL also supports continuous materialization of views, enhancing real-time analytics performance by avoiding costly recomputations. These features make Streaming SQL particularly valuable for real-time applications like analytics dashboards, event-driven systems, and real-time customer personalization, thus becoming an essential tool in modern data infrastructures as industries increasingly depend on real-time decision-making.