With the widespread use of GPS-equipped smartphones and Internet of Things devices, a huge amount of data with location information is being generated at an unprecedented rate. To gain a deeper insight into such a plethora of spatial data, scientists and engineers are widely using spatial queries for their big data applications. However, because of not only the massive spatial data size but also the complexity of spatial query processing, they are struggling to efficiently process the spatial queries. In this paper, we propose lightweight and scalable indexing and querying services for big spatial data stored in distributed storage systems or graph-based systems. Our spatial services have several advantages over existing approaches. First, our services can be easily applied to existing storage systems or graph-based models without modifying the internal implementation of existing systems/models. Second, our services achieve high pruning power by efficiently selecting only relevant spatial objects based on a simple yet effective filter. Third, our services support a customizable and easy-to-use control of index data size by adjusting the precision of indexed geometries. Lastly, our services support efficient updates of spatial data. Our experimental results using real-world datasets validate the effectiveness and efficiency of our spatial services.