Investigation of technologies of processing of big data

  • G. T. Balakayeva Faculty of Information Technologies, al-Farabi Kazakh National University, Almaty, Kazakhstan
  • D. K. Darkenbayev Faculty of Information Technologies, al-Farabi Kazakh National University, Almaty, Kazakhstan
  • Chris Phillips University of Newcastle upon Tyne, Newcastle, Great Britain

Abstract

 An overview of the technologies and methods is presented, on the basis of which the authors of this article model the processing of a large amount of data, developing a web application. In particular, it is proposed to combine models to improve the efficiency of processing large amounts of data. Large data set before traditional storage systems and processing a new challenge. This article analyzes possible methods their decisions, limitations that do not allow to do it effectively, and also provides an overview of three modern approaches to working with large data: NoSQL and real-time event flow processing. Analysis of large data requires the use of technology and the means to implement highly productive computing. The main factors of the problem are, first of all, the complexity and the second physical volume of the information collection. It should be noted that the actual processing of data includes the construction of the algorithm and the time for its description and debugging. Unique data collections require the development of unique algorithms, which increases the total processing time by an order of magnitude.

 
 


 

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АнглийскийИспанскийИтальянскийКазахскийКитайский ТрадКитайский УпрКорейскийРусскийТурецкийФранцузский
 
АнглийскийИспанскийИтальянскийКазахскийКитайский ТрадКитайский УпрКорейскийРусскийТурецкийФранцузский
 
 
 
 
 


 
 
 
Звуковая функция ограничена 200 символами

 

 
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Published
Dec 25, 2017
How to Cite
BALAKAYEVA, G. T.; DARKENBAYEV, D. K.; PHILLIPS, Chris. Investigation of technologies of processing of big data. International Journal of Mathematics and Physics, [S.l.], v. 8, n. 2, p. 13-18, dec. 2017. ISSN 2409-5508. Available at: <http://ijmph.kaznu.kz/index.php/kaznu/article/view/225>. Date accessed: 24 oct. 2018. doi: https://doi.org/10.1234/ijmph.v8i2.225.