Big Data encompasses collection, management, processing and analysis of the huge amount of data that varies in types and changes with high frequency. Often data component of Big Data has a positional component as an important part of it in various forms, such as postal address, Internet Protocol (IP) address and geographical location. If the positional components in Big Data extensively used in storage, retrieval, analysis, processing, visualization and knowledge discovery (geospatial Big Data) the Big Data systems need certain type of techniques and algorithms for management, analytics and sharing.
This paper describes the concept of geospatial Big Data management with focus on using typical and modern database management systems. Then the typical and modern types of databases for management of geospatial Big Data are evaluated based on model for storage, query languages, handling connected data, distribution models and schema evolution. As the results of the evaluations and benchmarks of this paper illustrate there is no single solution for efficient management of geospatial Big Data and in order to utilize unique characteristics of geospatial Big Data (such as topological, directional and distance relationship) a polyglot geospatial data persistence system is needed.