Spatial SQL is a query language that enables users to work with geometry and geography data types, allowing for faster geospatial data processing and analysis, as well as support for spatial modeling and machine learning. Unlike traditional SQL, Spatial SQL has deeper capabilities that assist in areas like analyzing points, lines, and polygons, and can be used to analyze data types such as addresses, place names, latitude and longitude coordinates, and more. The language provides numerous advantages, including increased efficiency in analytics workflows, support for cross-functional collaboration, and accessibility to the wider analytics community. Spatial SQL is typically used by GIS analysts, data scientists, developers, and teams working with spatial data in SQL-based databases or warehouses, but its adoption is growing among roles associated with less technical departments like marketing, business intelligence, and operations. The language has a relatively high learning curve, but the return on investment makes it worthwhile for those willing to learn it.