International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958 (Online), Volume-9 Issue-5, June 2020 21 Published By: Blue Eyes Intelligence Engineering & Sciences Publication © Copyright: All rights reserved. Retrieval Number: D9145049420/2020©BEIESP DOI: 10.35940/ijeat.D9145.069520 Journal Website: www.ijeat.org Abstract: It is well known that, at the present, a huge amount of information, often referred as Big Data, is processed by each domain of modern society. Big data are well defined by the seven dimensions: Volume, Velocity, Variety, Variability, Veracity, Visualization and Value. The traditional database management systems cannot handle the requirements of high availability, scalability and reliability emerged with Big Data. The good news is that we are now in the age of NoSQL databases. NoSQL do not have a fixed structure, they have a flexible structure and are suited for storing unstructured data produced in a large scale in various field. This work outlines the four main types of NoSQL databases and presents some of their representative solutions. Keywords : About four key words or phrases in alphabetical order, separated by commas. I. INTRODUCTION The IBM in a recent study, reports that in the last two years about the 90% of the data has been created. Each day the worlds outputs about 2.5 quintillion bytes a day. Domo, a computer software company specialized in business intelligence tools and data visualization, publishes the annual report (https://www.domo.com/learn/data-never-sleeps-7 ) Data Never Sleeps - that illustrates the online world’s activities happening within a minute. Figure I reports the data generated every minute in 2019. The IDC white paper by Seagate [38] forecasts that in 2025 enterprises will create more than 60% of data in the world. Therefore, in the next future the focus will be on the creation, utilization and management of data coming from governs, consumers and businesses. There is no doubt that the era of Big Data will continue to fast go on. Big data implies a big interest both from industries and academia. Big data research and applications are funded by many government agencies (e.g. the Obama Administration's Big Data Working Group report [39]) and topics related to Big Data are frequently reported by relevant public media such as the Economist [26, 39] and the New York Times [35]. Much effort is devoted by the research community on relevant Revised Manuscript Received on May 27, 2020. * Correspondence Author Ester Zumpamo*, DIMES, University of Calabria, Rende (CS), Italy. Email: : e.zumpano@ dimes.unical.it l.caroprese@dimes.unical.it Luciano Caroprese, DIMES, University of Calabria, Rende (CS), Italy. Email: l.caroprese@dimes.unical.it Eugenio Vocaturo, DIMES, University of Calabria, Rende (CS), Italy. Email: e.vocaturo@ dimes.unical.it; CNR-NANOTEC National Research Council, Rende, Italy. E-mail eugenio.vocaturo@cnr.it. © The Authors. Published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ ) challenges in Big Data. Premier conferences and prestigious journals, such as Nature and Science. Fig. I - Data generated every minute in 2019 II. BIG DATA We are surrounded by Big Data! Big data are extensively part of all human domains and all sectors of our society [9], even many problems are still to be solved in their management [47] and many challenges are open [49-51]. In the domain of healthcare Big Data are extensively used in medical information tools for data prediction and diagnostic procedures [1, 17-18, 27-28], as well as in computer vision and in machine learning techniques for melanoma lesions characterization and features detection [32-34, 40-42]. We are in the era of Big Data. But the question is: what is the exact meaning of Big Data? The reference to ``Big Data'' appeared in a paper written by researchers of NASA [3] in 1997. Many different definitions have been provided by recent literature [4, 10, 48], each acceptable and reporting a different vision, a different perspective of what is Big Data. In order to catch the real meaning of Big Data, as something different from big size data, many other dimensions have to be considered. In [11] Laney first, explicitly, uses the ``3 V's'', Volume, Velocity and Variety, to describe Big Data, even if he does not explicitly mention them. The ``3 V's'', as a mean to characterize Big Data, find a general consensus in the literature [10, 12, 19, 20, 21, 22]. Starting from this, many researchers and institutes such as IEEE focused on additional important features of Big Data such Value, Veracity, Visualization, Variability [4, 22, 29, 30]. Finally, the seven No SQL Database Management Systems for Big Data Luciano Caroprese, Ester Zumpano, Eugenio Vocaturo