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Journal articles on the topic 'Relational databases'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Tengeri, Dávid, and Ferenc Havasi. "Database Slicing on Relational Databases." Acta Cybernetica 21, no. 4 (2014): 629–53. http://dx.doi.org/10.14232/actacyb.21.4.2014.6.

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2

Maatuk, Abdelsalam, M. Akhtar Ali, and Nick Rossiter. "Converting Relational Databases into Object-relational Databases." Journal of Object Technology 9, no. 2 (2010): 145. http://dx.doi.org/10.5381/jot.2010.9.2.a3.

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3

Sharma, Yashraj, and Yashasvi Sharma. "CASE STUDY OF TRADITIONAL RDBMS AND NOSQL DATABASE SYSTEM." International Journal of Research -GRANTHAALAYAH 7, no. 7 (2019): 351–59. http://dx.doi.org/10.29121/granthaalayah.v7.i7.2019.777.

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On the basis of reliability, rational models are useful but not in terms of systems which involve huge amount of data; in such cases, non-relational models are much more useful. To store large chunks of data, NoSQL databases are used. NoSQL databases are scalable and wide ranged because they are non-relationally distributed. In relational databases, it was not possible to manage data which involved very large number of Big Data applications hence the concept of NoSQL database was introduced. There are a lot of advantages of NoSQL which not only involve its own features but also som

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Yashraj, Sharma, and Sharma Yashasvi. "CASE STUDY OF TRADITIONAL RDBMS AND NOSQL DATABASE SYSTEM." International Journal of Research - Granthaalayah 7, no. 7 (2019): 351–59. https://doi.org/10.5281/zenodo.3364448.

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On the basis of reliability, rational models are useful but not in terms of systems which involve huge amount of data; in such cases, non-relational models are much more useful. To store large chunks of data, NoSQL databases are used. NoSQL databases are scalable and wide ranged because they are non-relationally distributed. In relational databases, it was not possible to manage data which involved very large number of Big Data applications hence the concept of NoSQL database was introduced. There are a lot of advantages of NoSQL which not only involve its own features but also some features o

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5

Finkelstein, S., M. Schkolnick, and P. Tiberio. "Physical database design for relational databases." ACM Transactions on Database Systems 13, no. 1 (1988): 91–128. http://dx.doi.org/10.1145/42201.42205.

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6

Garvey, M. "Relational databases." Information and Software Technology 34, no. 12 (1992): 825. http://dx.doi.org/10.1016/0950-5849(92)90125-9.

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7

NAVNEET KUMAR, KASHYAP, PANDEY B.K, MANDORIA H.L, and KUMAR ASHOK. "A REVIEW OF LEADING DATABASES: RELATIONAL and NON-RELATIONAL DATABASE." i-manager's Journal on Information Technology 5, no. 2 (2016): 34. http://dx.doi.org/10.26634/jit.5.2.6002.

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8

Thakur, Nimesh, and Nishi Gupta. "Relational and Non Relational Databases: A Review." Journal of University of Shanghai for Science and Technology 23, no. 08 (2021): 117–21. http://dx.doi.org/10.51201/jusst/21/08341.

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Relational and non-relational databases are the two types of databases that are used to store data and perform dierent operations on it. For data storage, they use a variety of formats. In this paper, we’ll try to gure out what they’re all about and what the main dierences are. Databases serve as a data centre from which information is collected and processed. Data science is a multidisciplinary eld that combines mathematics, statistics, and programming to research data. For a data scientist, a basic understanding of databases is a must-have ability. We’ll look at how a data scientist can make

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Sliusarenko, Tetiana, and Valentin Filatov. "RELATIONAL VS NON-RELATIONAL DATABASES." Grail of Science, no. 23 (January 4, 2023): 269–71. http://dx.doi.org/10.36074/grail-of-science.23.12.2022.41.

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In this paper we’re going to talk about the difference between relational and non-relational databases. These are two different ways in which clients store the data that they have and operationalize it. And we know there is so much data that is coming into every single company today that it’s important that customers have options for how they want to store that data.

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10

Baliński, Patryk, Łukasz Chudy, and Maria Skublewska-Paszkowska. "Comparative analysis of the performance of relational and non-relational databases in applications implemented in C#." Journal of Computer Sciences Institute 34 (March 30, 2025): 44–53. https://doi.org/10.35784/jcsi.6688.

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The article focuses on comparing relational and non-relational databases using applications written in C#. The aim of the study is to understand in which cases relational databases are preferred and when it is worth considering the use of non-relational databases. The research examines the speed of data retrieval, updating, and deletion, in the context of five different databases, including relational ones like PostgreSQL, MySql, Oracle, and non-relational ones such as Neo4j and MongoDB. The data consists of 1,578,098 records. In the case of relational databases, a unified database model was a

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11

., Vinay Goyal. "REENGINEERING OF RELATIONAL DATABASES TO OBJECTORIENTED DATABASE." International Journal of Research in Engineering and Technology 03, no. 01 (2014): 112–15. http://dx.doi.org/10.15623/ijret.2014.0301018.

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12

Nisa, Behjat U. "A Comparison between Relational Databases and NoSQL Databases." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 845–48. http://dx.doi.org/10.31142/ijtsrd11214.

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13

Bulyha, Kostiantin, Olena Bulyha, and Mykola Huzii. "Visualization of Relational Databases." Digital Platform: Information Technologies in Sociocultural Sphere 5, no. 1 (2022): 83–89. https://doi.org/10.31866/2617-796X.5.1.2022.261292.

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The purpose of the article is to demonstrate the relational databases visualization algorithm using MS Power BI software products in MS Excel. The research methodology is cloud information processing technologies. The novelty of the study is the implementation of the relational database visualization algorithm using MS Power BI software products in MS Excel. Conclusions. Using MS Power Query and MS Power Pivot software in MS Excel allows you to present visually summary information from databases. The example of connecting these products to the database management system (DB

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14

Alekseev, Konstantin. "Relational database problems." Кибернетика и программирование, no. 2 (February 2020): 7–18. http://dx.doi.org/10.25136/2644-5522.2020.2.34076.

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The relevance of this article lies in the fact that today's databases are the basis of numerous information systems.&nbsp;The information accumulated in them is extremely valuable material, and today database processing methods are widely spread in terms of extracting additional methods, knowledge from them, which are interconnected with generalization and various additional methods of information processing.The object of research in this work is relational databases and DBMS, the subject of research is the features of their use in applied programming.In accordance with the set goal, it is

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15

K.Rathva, Mayuree, and Sahani G.J. "Watermarking Relational Databases." International Journal of Computer Science, Engineering and Applications 3, no. 1 (2013): 71–79. http://dx.doi.org/10.5121/ijcsea.2013.3107.

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Yang, Xiaoyan, Cecilia M. Procopiuc, and Divesh Srivastava. "Summarizing relational databases." Proceedings of the VLDB Endowment 2, no. 1 (2009): 634–45. http://dx.doi.org/10.14778/1687627.1687699.

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17

Seltzer, Margo. "Beyond Relational Databases." Queue 3, no. 3 (2005): 50–58. http://dx.doi.org/10.1145/1059791.1059807.

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18

Seltzer, Margo. "Beyond relational databases." Communications of the ACM 51, no. 7 (2008): 52–58. http://dx.doi.org/10.1145/1364782.1364797.

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19

Jackson, MS. "Beyond relational databases." Information and Software Technology 32, no. 4 (1990): 258–65. http://dx.doi.org/10.1016/0950-5849(90)90059-z.

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20

Reznichenko, V. A. "60 Years of Databases." PROBLEMS IN PROGRAMMING, no. 3 (September 2021): 040–71. http://dx.doi.org/10.15407/pp2021.03.040.

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The article provides an overview of research and development of databases since their appearance in the 60s of the last century to the present time. The following stages are distinguished: the emergence formation and rapid development, the era of relational databases, extended relational databases, post-relational databases and big data. At the stage of formation, the systems IDS, IMS, Total and Adabas are described. At the stage of rapid development, issues of ANSI/X3/SPARC database architecture, CODASYL proposals, concepts and languages of conceptual modeling are highlighted. At the stage of

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Nakhare, Disha. "A Comparative study of SQL Databases and NoSQL Databases for E-Commerce." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 409–12. http://dx.doi.org/10.22214/ijraset.2021.39263.

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Abstract: With the advent of E-Commerce, businesses persistently examine various ways to improvise and accomplish their demands with web engineering that provide notable resolution. The progress in economic status demands colossal databases that store the data efficiently. The databases currently used are relational or non-relational. Both these types have their benefits and limitations that influence the overall processing of data. Non-relational databases are referred to as NoSQL-not only SQL, and Relational databases are known as SQL-Structured Query Language. It has been suggested in many

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Reznichenko, V. A. "60 Years of Databases (part three)." PROBLEMS IN PROGRAMMING, no. 1 (March 2022): 034–66. http://dx.doi.org/10.15407/pp2022.01.034.

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The article provides an overview of research and development of databases since their appearance in the 60s of the last century to the present time. The following stages are distinguished: the emergence formation and rapid development, the era of relational databases, extended relational databases, post-relational databases and big data. At the stage of formation, the systems IDS, IMS, Total and Adabas are described. At the stage of rapid development, issues of ANSI/X3/SPARC database architecture, CODASYL proposals, concepts and languages of conceptual modeling are highlighted. At the stage of

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Reznichenko, V. A. "60 Years of Databases (part two)." PROBLEMS IN PROGRAMMING, no. 4 (December 2021): 036–61. http://dx.doi.org/10.15407/pp2021.04.036.

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The article provides an overview of research and development of databases since their appearance in the 60s of the last century to the present time. The following stages are distinguished: the emergence formation and rapid development, the era of relational databases, extended relational databases, post-relational databases and big data. At the stage of formation, the systems IDS, IMS, Total and Adabas are described. At the stage of rapid development, issues of ANSI/X3/SPARC database architecture, CODASYL proposals, concepts and languages of conceptual modeling are highlighted. At the stage of

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Reznichenko, V. A. "60 Years of Databases (final part)." PROBLEMS IN PROGRAMMING, no. 1 (January 2023): 66–103. http://dx.doi.org/10.15407/pp2023.01.066.

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The article provides an overview of research and development of databases since their appearance in the 60s of the last century to the present time. The following stages are distinguished: the emergence formation and rapid development, the era of relational databases, extended relational databases, post-relational databases and big data. At the stage of formation, the systems IDS, IMS, Total and Adabas are described. At the stage of rapid development, issues of ANSI/X3/ SPARC database architecture, CODASYL proposals, concepts and languages of conceptual modeling are highlighted. At the stage o

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25

Behjat, U. Nisa. "A Comparison between Relational Databases and NoSQL Databases." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 845–48. https://doi.org/10.31142/ijtsrd11214.

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Databases are used for storing and managing large amounts of data. Relational model is useful when it comes to reliability but when it comes to the modern applications dealing with large amounts of data and the data is unstructured non relational models are usable. NoSQL databases are used to store large amounts of data. NoSQL databases are non relational, distributed, open source and are horizontally scalable. This paper provides the comparison of the relational model with NoSQL Behjat U Nisa "A Comparison between Relational Databases and NoSQL Databases" Published in International

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26

Bordoloi, Subhrajyoti, and Bichitra Kalita. "Designing Graph Database Models from Existing Relational Databases." International Journal of Computer Applications 74, no. 1 (2013): 25–31. http://dx.doi.org/10.5120/12850-9303.

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27

Qadah and Irani. "A Database Machine for Very Large Relational Databases." IEEE Transactions on Computers C-34, no. 11 (1985): 1015–25. http://dx.doi.org/10.1109/tc.1985.1676534.

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28

Reshma, K.R, K.R Reshma, and Mariam Varghese Surekha. "OUTCOME ANALYSIS IN ACADEMIC INSTITUTIONS USING NEO4J." International Journal of Computational Science and Information Technology (IJCSITY) 4, MAY (2016): 1–10. https://doi.org/10.5281/zenodo.3463026.

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ABSTRACT Databases are an integral part of a computing system and users heavily rely on the services they provide. When interact with a computing system, we expect that data be stored for future use, that the data is able to be looked up fastly, and we can perform complex queries against the data stored in the database. Many different emerging database types available for use such as relational databases, object databases, keyvalue databases, graph databases, and RDF databases. Each type of database provides unique qualities that have applications in certain domains. Our work aims to investiga

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29

Zmaranda, Doina R., Cristian I. Moisi, Cornelia A. Győrödi, Robert Ş. Győrödi, and Livia Bandici. "An Analysis of the Performance and Configuration Features of MySQL Document Store and Elasticsearch as an Alternative Backend in a Data Replication Solution." Applied Sciences 11, no. 24 (2021): 11590. http://dx.doi.org/10.3390/app112411590.

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In recent years, with the increase in the volume and complexity of data, choosing a suitable database for storing huge amounts of data is not easy, because it must consider aspects such as manageability, scalability, and extensibility. Nowadays, the NoSQL databases have gained immense popularity for their efficiency in managing such datasets compared to relational databases. However, relational databases also exhibit some advantages in certain circumstances, therefore many applications use a combined approach: relational and non-relational. This paper performs a comparative evaluation of two p

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30

Keivani, Negin, Abdelsalam M. Maatuk, Shadi Aljawarneh, and Muhammad Akhtar Ali. "Towards the Maturity of Object-Relational Database Technology: Promises and Reality." International Journal of Technology Diffusion 6, no. 4 (2015): 1–19. http://dx.doi.org/10.4018/ijtd.2015100101.

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Object-relational technology provides a significant increase in scalability and flexibility over the traditional relational databases. The additional object-relational features are particularly satisfying for advanced database applications that relational database systems have experienced difficulties. The key factor to the success of object-relational database systems is their performance. This paper aims to review the promises of Object-Relational database systems, examine the reality, and how their promises may be fulfilled through unification with the relational technology. To investigate

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Zhou, Peng, Mei Li, Jing Huang, and Hua Fang. "Research on Database Schema Comparison of Relational Databases and Key-Value Stores." Advanced Materials Research 1049-1050 (October 2014): 1860–63. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1860.

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With the rapid development of Internet technology, the management capacity of traditional relational databases becomes relatively inefficient when facing the access and processing of big data. As a kind of non-relational databases, the key-value stores, with its high scalability, provide an efficient solution to the problem. This article introduces the concept and features of Key-Value stores, and followed by the comparison with the traditional relational databases, and an example is illustrated to explain its typical application and finally the existing problems of Key-Value stores are summar

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Reshma, K.R, Femy P.F Mary, and Mariam Varghese Surekha. "OUTCOME ANALYSIS IN ACADEMIC INSTITUTIONS USING NEO4J." International Journal of Computational Science and Information Technology (IJCSITY) 4, no. 2 (2016): 1–10. https://doi.org/10.5281/zenodo.3698858.

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<strong>ABSTRACT </strong> Databases are an integral part of a computing system and users heavily rely on the services they provide. When interact with a computing system, we expect that data be stored for future use, that the data is able to be looked up fastly, and we can perform complex queries against the data stored in the database. Many different emerging database types available for use such as relational databases, object databases, keyvalue databases, graph databases, and RDF databases. Each type of database provides unique qualities that have applications in certain domains. Our work

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33

Kunda, Douglas, and Hazael Phiri. "A Comparative Study of NoSQL and Relational Database." Zambia ICT Journal 1, no. 1 (2017): 1–4. http://dx.doi.org/10.33260/zictjournal.v1i1.8.

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Relational Database and NoSQL are competing types of database models. The former has been in existence since 1979 and the latter since the year 2000. The demands of modern applications especially in web 2.0, 3.0 and big data have made NoSQL a popular database of choice. Choosing an appropriate database model to use is an important decision that developers must make based on the features of a given database model. This paper compares the features of Relational Databases and NoSQL to establish which database is better at supporting demands of modern applications. The paper also brings out the ch

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34

Chung, Jen-Yao, Yi-Jing Lin, and Daniel T. Chang. "Object and relational databases." ACM SIGPLAN OOPS Messenger 6, no. 4 (1995): 164–69. http://dx.doi.org/10.1145/260111.260273.

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35

Llorens, J., and A. Trénor. "MARC and relational databases." Electronic Library 11, no. 2 (1993): 93–96. http://dx.doi.org/10.1108/eb045213.

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36

Kocharekar, Raju. "Nulls in relational databases." ACM SIGMOD Record 18, no. 1 (1989): 68–73. http://dx.doi.org/10.1145/382272.382416.

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37

Wei, Ling Ling, and Wei Yang. "A Constructed Method of the Hash Function for the Rough Relational Databases." Applied Mechanics and Materials 427-429 (September 2013): 2588–91. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2588.

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Rough relational database model provided a processing method for uncertainty data, So based on the Hash technology and the data characteristic of rough relational database, it was researched the multiple value data item in the rough relational database represented by binary string in virtue of equivalence classes, calculated its decimal value, and constructed Hash Function. Then according to the decimal number distributed Hash address where stored the data of the rough relational databases. Finally, an algorithm for constructed method of Hash function for the rough relational databases was des

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Reznichenko, V. A. "60 Years of Databases (part four)." PROBLEMS IN PROGRAMMING, no. 2 (June 2022): 57–95. http://dx.doi.org/10.15407/pp2022.02.057.

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The article provides an overview of research and development of databases since their appearance in the 60s of the last century to the present time. The following stages are distinguished: the emer- gence formation and rapid development, the era of relational databases, extended relational data- bases, post-relational databases and big data. At the stage of formation, the systems IDS, IMS, Total and Adabas are described. At the stage of rapid development, issues of ANSI/X3/SPARC database architecture, CODASYL proposals, concepts and languages of conceptual modeling are highlighted. At the stag

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39

Beech, David, and Çetin Özbütün. "Object databases as generalizations of relational databases." Computer Standards & Interfaces 13, no. 1-3 (1991): 221–30. http://dx.doi.org/10.1016/0920-5489(91)90030-4.

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Karunaratna, Damitha D. "BUILDING ONTOLOGIES OVER RELATIONAL DATABASES." International Journal of Research -GRANTHAALAYAH 6, no. 11 (2018): 254–65. http://dx.doi.org/10.29121/granthaalayah.v6.i11.2018.1123.

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Relational Databases are typically created to fulfil the information requirements of a community of users generally belongs to a single organization. Data stored in these databases were typically accessed by using Structured Query Languages or through customized interfaces. With the popularity of the World Wide Web and the availability of large number of Relational Databases for public access there is a need for users to retrieve data from these databases by using a text-based queries, possibly by using the terms that they are familiar with. However, the inherent limitations of Structured Quer

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Damitha, D. Karunaratna. "BUILDING ONTOLOGIES OVER RELATIONAL DATABASES." International Journal of Research - Granthaalayah 6, no. 11 (2018): 254–65. https://doi.org/10.5281/zenodo.1929732.

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Relational Databases are typically created to fulfil the information requirements of a community of users generally belongs to a single organization. Data stored in these databases were typically accessed by using Structured Query Languages or through customized interfaces. With the popularity of the World Wide Web and the availability of large number of Relational Databases for public access there is a need for users to retrieve data from these databases by using a textbased queries, possibly by using the terms that they are familiar with. However, the inherent limitations of Structured Query

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42

Győrödi, Cornelia A., Tudor Turtureanu, Robert Ş. Győrödi, and Doina R. Zmaranda. "Implementing a Synchronization Method between a Relational and a Non-Relational Database." Big Data and Cognitive Computing 7, no. 3 (2023): 153. http://dx.doi.org/10.3390/bdcc7030153.

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The accelerating pace of application development requires more frequent database switching, as technological advancements demand agile adaptation. The increase in the volume of data and at the same time, the number of transactions has determined that some applications migrate from one database to another, especially from a relational database to a non-relational (NoSQL) alternative. In this transition phase, the coexistence of both databases becomes necessary. In addition, certain users choose to keep both databases permanently updated to exploit the individual strengths of each database in or

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Minukhin, Serhii. "PERFORMANCE STUDY OF THE DTU MODEL FOR RELATIONAL DATABASES ON THE AZURE PLATFORM." Innovative Technologies and Scientific Solutions for Industries, no. 1 (19) (April 26, 2022): 27–39. http://dx.doi.org/10.30837/itssi.2022.19.027.

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When solving problems of working with relational databases on cloud platforms, the problem arises of choosing a specific model to ensure the performance of executing queries of varying complexity. The object of research is the processes of implementing various types of queries to relational databases within the framework of the DTU purchase model of the MS Azure platform. The subject is methods for evaluating the performance of work with relational databases based on the timing of query execution and indicators of the load on the resources of the cloud platform. The aim of the study is to deve

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Thomer, Andrea K., and Karen M. Wickett. "Relational data paradigms: What do we learn by taking the materiality of databases seriously?" Big Data & Society 7, no. 1 (2020): 205395172093483. http://dx.doi.org/10.1177/2053951720934838.

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Although databases have been well-defined and thoroughly discussed in the computer science literature, the actual users of databases often have varying definitions and expectations of this essential computational infrastructure. Systems administrators and computer science textbooks may expect databases to be instantiated in a small number of technologies (e.g., relational or graph-based database management systems), but there are numerous examples of databases in non-conventional or unexpected technologies, such as spreadsheets or other assemblages of files linked through code. Consequently, w

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Pokorný, Jaroslav. "Integration of Relational and NoSQL Databases." Vietnam Journal of Computer Science 06, no. 04 (2019): 389–405. http://dx.doi.org/10.1142/s2196888819500210.

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The analysis of relational and NoSQL databases leads to the conclusion that these data processing systems are to some extent complementary. In the current Big Data applications, especially where extensive analyses (so-called Big Analytics) are needed, it turns out that it is nontrivial to design an infrastructure involving data and software of both types. Unfortunately, the complementarity negatively influences integration possibilities of these data stores both at the data model and data processing levels. In terms of performance, it may be beneficial to use a polyglot persistence, a multimod

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Imam, Abdullahi Abubakar, Shuib Basri, Rohiza Ahmad, et al. "DSP: Schema Design for Non-Relational Applications." Symmetry 12, no. 11 (2020): 1799. http://dx.doi.org/10.3390/sym12111799.

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The way a database schema is designed has a high impact on its performance in relational databases, which are symmetric in nature. While the problem of schema optimization is even more significant for NoSQL (“Not only SQL”) databases, existing modeling tools for relational databases are inadequate for this asymmetric setting. As a result, NoSQL modelers rely on rules of thumb to model schemas that require a high level of competence. Several studies have been conducted to address this problem; however, they are either proprietary, symmetrical, relationally dependent or post-design assessment to

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Yoan, Antonio Lopez Rodriguez, Hidalgo Delgado Yusniel, and Silega Martinez Nemury. "Linkage scenarios of relational databases and ontologies: a systematic mapping." Enfoque UTE 12, no. 4 (2021): 58–75. https://doi.org/10.29019/enfoqueute.759.

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Relational databases are one of the most used data sources. However, as a storage source, they present a group of shortcomings. It is complex to store semantic knowledge in relational databases. To solve the deficiencies in knowledge representation of relational databases, one trend has been to use ontologies. Ontologies possess a richer semantic and are closer to the end user vocabulary than relational database schemas. The objective of the present research was to carry out a systematic mapping about the scenarios where relational databases and ontologies are linked to provide a better integr

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Princz, Mária. "Trends and Challenges of Databases." International Journal of Engineering and Management Sciences 3, no. 5 (2018): 71–75. http://dx.doi.org/10.21791/ijems.2018.5.8.

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The database management, using relational databases, is part of curriculum in the Hungarian high schools. The aim of this paper is to present how we can show for students the challenges facing data processing, data retrieval, beyond the relational database management taught in high school.

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Blessing, E. James. "HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENT." International Journal of Computer Science, Engineering and Applications (IJCSEA) 7, no. 3/4 (2020): 15–27. https://doi.org/10.5281/zenodo.3674667.

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Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that t

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Ait El Mouden, Zakariyaa, and Abdeslam Jakimi. "A New Algorithm for Storing and Migrating Data Modelled by Graphs." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 11 (2020): 137. http://dx.doi.org/10.3991/ijoe.v16i11.15545.

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<span>NoSQL databases have moved from theoretical solutions to exceed relational databases limits to a practical and indisputable application for storing and manipulation big data. In term of variety, NoSQL databases store heterogeneous data without being obliged to respect a predefined schema such as the case of relational and object-relational databases. NoSQL solutions surpass the traditional databases in storage capacity; we consider MongoDB for example, which is a document-oriented database capable of storing unlimited number of documents with a maximal size of 32TB depending on the

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