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Spatial data is pervasive. Large amount of spatial data is produced every day from GPS-enabled devices such as cell phones, cars, sensors, and various consumer based applications such as Uber, location-tagged posts in Facebook, In-stagram, Snapchat, etc. This growth in spatial data coupled with the fact that spatial queries, analytical or transactional, can be computationally extensive has attracted enormous interest from the research community to develop systems that can efficiently process and analyze this data. In recent years a lot of spatial analytics systems have emerged. Existing work compares either limited features of these systems or the studies are outdated since new systems have emerged. In this work, we first explore the available modern spatial processing systems and then thoroughly compare them based on features and queries they support, using real-world datasets.