With the relational database model being more than 40 years old, combined with the continuously increasing use of ‘big data’, NoSQL systems are marketed as providing a more efficient means of dealing with large quantities of usually unstructured data. NoSQL systems may provide advantages over relational databases but generally lack the relational robustness for those advantages. This project attempts to contribute to the GIS field in comparing Open-Source RDBMS and NoSQL systems, storing and querying spatial data with the overall goal to determine if NoSQL systems (specifically MongoDB ) have a place within the GI world. Working with Open-source spatial dataset, OpenStreetMap, a scalable approach is taken working through global to local scaled data. This approach aims to provide insight to how either system may present performance advantages related to data size. The research highlights how the performance of each system is limited by the system functionality. MongoDB’s spatial capabilities are lacking in comparison to the PostgreSQL spatial extension PostGIS. The outcome is that MongoDB cannot support the spatial needs of a specialist GIS operative currently, however if basic spatial functionality is all that is needed, MongoDB presents high performance on large datasets. PostGIS has a complex, highly specialist ream of spatial functionally making it the best performing spatial system, however increasing dataset size does present a system slow down relationship. The use of each system is dependent on the application but at the present time this NoSQL system is spatially outclassed thus not worthy of the specialist GIS industry.