Persisting big-data: The NoSQL landscape. (January 2017)

• Record Type: Journal Article
• Authors: Corbellini, Alejandro; Mateos, Cristian; Zunino, Alejandro; Godoy, Daniela; Schiaffino, Silvia
• Is Part Of: Information systems. Volume 63(2017)
• Page Start: 1
• Page End: 23
• Publication Date: 2017-01
• Subjects: NoSQL databases -- Relational databases -- Distributed systems -- Database persistence -- Database distribution -- Big data; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2016.07.009
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: The growing popularity of massively accessed Web applications that store and analyze large amounts of data, being Facebook, Twitter and Google Search some prominent examples of such applications, have posed new requirements that greatly challenge traditional RDBMS. In response to this reality, a new way of creating and manipulating data stores, known as NoSQL databases, has arisen. This paper reviews implementations of NoSQL databases in order to provide an understanding of current tools and their uses. First, NoSQL databases are compared with traditional RDBMS and important concepts are explained. Only databases allowing to persist data and distribute them along different computing nodes are within the scope of this review. Moreover, NoSQL databases are divided into different types: Key-Value, Wide-Column, Document-oriented and Graph-oriented. In each case, a comparison of available databases is carried out based on their most important features. Abstract : Highlights: We review existing NoSQL database systems for persisting big-data and their main concepts. We group databases according to their structural similarity and compare them across different dimensions. We analyze problems and future developments in the area.