The importance of big data technology

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    The adoption of Web 2.0 technologies, Internet of Things, etc. by individuals and organization has led to an explosion of data. As it stands, existing Relational Database Management Systems (RDBMSs) are incapable of handling this deluge of data. The term Big Data was coined to represent these vast, fast and complex datasets that regular RDBMSs could not handle. Special tools or frameworks were developed to deal with processing, managing and storing this big data. These tools are capable of functioning in distributed industry- standard environments thereby maintaining efficiency and effectiveness at a business level. Apache Hadoop is an example of such a framework. This report discusses big data, it origins, opportunities and challenges that it presents, big data analytics and the application of big data using existing big data tools or frameworks. It also discusses Apache Hadoop as a big data framework and provides a basic overview of this technology from technological and business perspectives.

     


     

  • Keywords


    Big Data; Big Data Analytics; Relational Database Management Systems; Apache Hadoop

  • References


      [1] Akamai Technologies. (2017). Akamai's State of the Internet.

      [2] Apteryx & Hortonworks. (2013). the Business Analyst's Guide to Hadoop.

      [3] Bhadani, A. K., & Jothimani, D. (2016). Big Data: Challenges, Opportunities and Realities. Delhi, India.

      [4] Facebook. (2015). Retrieved 2017, from Statistic Brain: http://statisticbrain.com/facebook-statistics.

      [5] IBM. (2017, October). IBM Watson Analytics. Retrieved 2017, from IBM: https://www.ibm.com/us-en/marketplace/watson- analytics.

      [6] Intel. (2013). Extract, Transform, and Load Big Data with Apache Hadoop.

      [7] N. G. Zagoruiko, I. A. Borisova, V. V. Dyubanov and O. A. Kutnenko. A quantitative measure of compactness and similari- ty in a competitive space. Journal of Applied and Industrial Mathematics, 2011, Vol. 5, № 1, pp.144-154.

      [8] N. G. Zagoruiko, I. A. Borisova, O. A. Kutnenko, V. V. Dyu- banov. A construction of a compressed description of data us- ing a function of rival similarity. Journal of Applied and Indus- trial Mathematics, April 2013, Volume 7, Issue 2, pp 275-286.

      [9] Madden, S. (2012). From Databases to Big Data. IEEE Internet Computing, p. 4.

      [10] McMahon GT, Gomes HE, Hohne SH, Hu TM, Levine BA &Conlin PR (2005). Web-based care management in patients with poorly controlled diabetes. Diabetes Care 28, 1624–1629. http://www.tadviser.ru/index.php/http://lpgenerator.ru/blog/201 6/04/01/obzor-5-instrumentov-dlya-sozdaniya-udivitelnyh-onlajn-grafikov.

      [11] Moise, I., & Pournaras, E. (2017). Big Data Analytics.

      [12] Pavlovskiy E.N. master is Program “Big Data Analytics” In Novosibirsk State University. International conference on Clouds, Big Data and Trust (ICCBDT- 2013), 13-15 November 2013, Bhopal, India.

      [13] Russom, P. (2013). Integrating Hadoop into Business Intelli- gence and Data Warehousing.

      [14] Samson, O. F., Serdar, S., Vanduhe, V. Z. Advancing big data for humanitarian needs. Humanitarian Technology: Science, Systems and Global Impact 2014, HumTech (2014).

      [15] Thakurdesai PA, Kole PL & Pareek RP (2004). Evaluation of the quality and contents of diabetes mellitus patient education on Internet. Patient Education and Counseling 53, 309–313.


 

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Article ID: 21139
 
DOI: 10.14419/ijet.v7i4.5.21139




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