SQL Versus NoSQL Movement with Big Data Analytics

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Sitalakshmi Venkatraman 1,* Kiran Fahd 1 Samuel Kaspi 1 Ramanathan Venkatraman 2

1. School of Engineering, Construction and Design (IT), Melbourne Polytechnic, VIC 3072, Australia

2. National University of Singapore, Singapore

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2016.12.07

Received: 17 Feb. 2016 / Revised: 3 Jun. 2016 / Accepted: 11 Aug. 2016 / Published: 8 Dec. 2016

Index Terms

Structured Query Language (SQL), Non SQL (NoSQL), Big Data, Big Data Analytics, Relational Database, SQL Database, NoSQL Database


Two main revolutions in data management have occurred recently, namely Big Data analytics and NoSQL databases. Even though they have evolved with different purposes, their independent developments complement each other and their convergence would benefit businesses tremendously in making real-time decisions using volumes of complex data sets that could be both structured and unstructured. While on one hand many software solutions have emerged in supporting Big Data analytics, on the other, many NoSQL database packages have arrived in the market. However, they lack an independent benchmarking and comparative evaluation. The aim of this paper is to provide an understanding of their contexts and an in-depth study to compare the features of four main NoSQL data models that have evolved. The performance comparison of traditional SQL with NoSQL databases for Big Data analytics shows that NoSQL database poses to be a better option for business situations that require simplicity, adaptability, high performance analytics and distributed scalability of large data. This paper concludes that the NoSQL movement should be leveraged for Big Data analytics and would coexist with relational (SQL) databases.

Cite This Paper

Sitalakshmi Venkatraman, Kiran Fahd, Samuel Kaspi, Ramanathan Venkatraman, "SQL Versus NoSQL Movement with Big Data Analytics", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.12, pp.59-66, 2016. DOI:10.5815/ijitcs.2016.12.07


[1]Choi, Y., Jeon, W., & Yo, S. (2014), 'Improving Database System Performance by Applying NoSQL', Journal Of Information Processing Systems, 10(3), 355-364.

[2]Moniruzzaman, A. B., & Hossain, S. A. (2013), NoSQL database: New era of databases for big data analytics - Classification, characteristics and comparison. International Journal of Database Theory and Application, 6(4), 1-14.

[3]Bazar, C., & Losif, C. (2014), 'The Transition from RDBMS to NoSQL. A Comparative Analysis of Three Popular Non-Relational Solutions: Cassandra, MongoDB and Couchbase', Database Systems Journal, 5(2), 49-59.

[4]Floratou, A., Teletia, N., Dewitt, D., Patel, J., & Zhang, D. (2012), 'Can the Elephants Handle the NoSQL Onslaught?', VLDB Endowment, 5(12), 1712-1723.

[5]Mason, R. T. (2015), 'NoSQL databases and data modeling techniques for a document-oriented NoSQL database', Proceedings of Informing Science & IT Education Conference (InSITE) 2015, 259-268.

[6]Pothuganti, A.  (2015) 'Big Data Analytics: Hadoop-Map Reduce & NoSQL Databases', International Journal of Computer Science and Information Technologies, 6(1), 522-527.

[7]Smolan, R. & Erwit, J. (2012), The Human face of Big Data, Against all odds production, O’Reilly, USA.

[8]Sharda, R., Delen, D., & Turban, E. (2015), Business intelligence and analytics: systems for decision support (10th ed.). Upper Saddle River, NJ: Pearson. 

[9]Ohlhorst, F. (2013), Big data analytics: Turning big data into big money. Hoboken, NJ. John Wiley and Sons.

[10]Kaur, P.D., Kaur, A. & Kaur, S. (2015), ‘Performance Analysis in Bigdata’, International Journal of Information Technology and Computer Science (IJITCS), 7(11), 55-61.

[11]Prasad, A, & Gohil, B. (2014), 'A Comparative Study of NoSQL Databases', International Journal Of Advanced Research In Computer Science, 5(5), 170-176.

[12]Nayak, A., Poriya, A. & Poojary, D. (2013), ‘Type of NOSQL Databases and its Comparison with Relational Databases’, International Journal of Applied Information Systems (IJAIS), 5(4) Foundation of Computer Science FCS, New York, USA.

[13]MongoDB (2014), ‘Why NoSQL?’, https://www.mongodb.com/nosql-explained, [Online: accessed 20-Feb-2016] 

[14]Fotache, M., & Cogean, D. (2013), 'NoSQL and SQL Databases for Mobile Applications. Case Study: MongoDB versus PostgreSQL', Informatica Economica, 17(2), 41-58.

[15]Ullah, Md A. (2015),  ‘A Digital Library for Plant Information with Performance Comparison between a Relational Database and a NoSQL Database (RDF Triple Store)’, Technical Library, Paper 205.

[16]Hurst, N. (2010, ‘Visual Guide to NoSQL Systems’, http://blog.nahurst.com/visual-guide-to-nosql-systems, [Online: accessed 5-Nov-2015]

[17]Planet Cassandra ‘NoSQL Databases Defined and Explained’, http://www.planetcassandra.org/what-is-nosql,[Online: accessed 24-Mar-2016] 

[18]Klein, J., Gorton, I., Ernst, N. & Donohoe, P. (2015), 'Performance Evaluation of NoSQL Databases: A Case Study', Proceedings of the 1st Workshop on Performance Analysis of Big Data Systems, PABS’15, Austin, 5-10.

[19]MongoDB (2015), ‘Top 5 Considerations When Evaluating NoSQL Databases’, https://s3.amazonaws.com/ info-mongodb-com/10gen_Top_5_NoSQL_Considerations.pdf [Online: accessed 5-Nov-2015] 

[20]Zhikun, C., Shuqiang, Y., Shuan, T., Hui, Z., Li., Ge, Z.,& Huiyu, Z. (2014), ‘The Data Allocation Strategy Based on Load in NoSQL Database’, Applied Mechanics and Materials, 513-517, 1464-1469. 

[21]Leavitt, N. (2010), ‘Will NoSQL Databases Live Up to Their Promise?’, IEEE Computer  43(2) , 12-14.

[22]Subramanian, S. (2012), ‘NoSQL: An Analysis of the Strengths and Weaknesses’, https://dzone.com/articles/nosql-analysis-strengths-and, [Online: accessed 15-Jan-2016] 

[23]Prasad B.R. & Agarwal S. (2016), 'Comparative Study of Big Data Computing and Storage Tools: A Review',  International Journal of Database Theory and Application 9(1), 45-66.

[24]Warden P. (2012), Big Data Glossary - A Guide to the New Generation of Data Tools, O’Reilly, USA.

[25]Zareian S., Fokaefs, M., Khazaei H. Litoiu M. & Zhang X. (2016), 'A Big Data Framework for Cloud Monitoring',  Proceedings of the 2nd International Workshop on BIG Data Software Engineering (BIGDSE'16), ACM Digital Library,  58-64.

[26]Ramanathan, V. & Venkatraman, S. (2015), Cloud Adoption in Enterprises: Security  Issues and Strategies, 96-121, Book Chapter In Haider A. and Pishdad A. (Eds.), Business Technologies in Contemporary Organizations: Adoption, Assimilation, and Institutionalization, IGI Global Publishers, USA.