Tassonomy and Review of Big Data Solutions Navigation

In recent years more and more often, we hear about Big Data and issues related to the management of these huge volume of data, and considering that there is no definitive solution for their storage, querying, analysis. This discipline is in great evolution, multidisciplinary and very complex to be dominated for the several aspects to be addressed: architectural, data structure, data management, data analytics, protection and security, computational as parallel and distributed processing, etc. To cope with these problems a basic survey of main tips, which would allow the developer to orient themselves in the choice of the best solution, for the development of an architecture for the management of Big Data, in each specific case could be of great support and help. In fact a large number of fields, from industry to scientific research, are inquiring information and hints about what can be obtained through the analysis of Big Data and how these infrastructures can be created, by using what, etc. And, therefore, more powerful solutions are needed to cope with increasing complexity and variety of problems, for their better management and exploitation of the open accessible, produced and integrated data. In this paper, starting from the analysis of the existing solutions, particularly interesting and well documented use cases, we identified a group of main differentiating features which can mainly influence the choice of the solution to be set up; then we looked at different types of existing solutions and products to see how they are handled the identified features. Lastly, the results obtained with the analysis of the desirable features for each main domain and then the identification of the most suitable and/or adopted products for the application domains. The work cannot be exhaustive, and in many cases we had to decide to include and to exclude aspects and tools. The obtained results can be regarded as a model and main guidelines for big data solution navigation.