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Big data systems development is full of challenges in view of the variety ofapplication areas and domains that this technology promises to serve.Typically, fundamental design decisions involved in big data systems designinclude choosing appropriate storage and computing infrastructures. In this ageof heterogeneous systems that integrate different technologies for developmentof an optimized solution to a specific real world problem, big data systems arenot an exception to any such rule. As far as the storage aspect of any big datasystem is concerned, the primary facet in this regard is a storageinfrastructure and NoSQL is the right technology that fulfills itsrequirements. However, every big data application has variable datacharacteristics and thus, the corresponding data fits into a different datamodel. Moreover, the requirements of different applications vary on the basisof budget and functionality. This paper presents a feature analysis of 80 NoSQLsolutions, elaborating on the criteria and points that a developer mustconsider while making a possible choice. Bivariate analysis of dataset createdfor the identified NoSQL solutions was performed to establish relationshipbetween 9 features. Furthermore, cluster analysis of the dataset was used tocreate categories of solutions to present a statistically supportedclassification scheme. Finally, applications for different solutions werereviewed and classified under domain-specific categories. Random forestclassification was used to determine the most relevant features forapplications and correspondingly a decision tree-based prediction model wasproposed, implemented and deployed in the form of a web application todetermine the suitability of a NoSQL solution for an application area.

Bivariate, Cluster and Suitability Analysis of NoSQL Solutions for Different Application Areas