Big Data Architectures and Concepts

Author:

Tembo Welo Audrey,Lubaki Kinzonzi Hervé,Bila Khonde Noel,Mbuyi Mukendi Eugène

Abstract

Nowadays, the processing of big data has become a major preoccupation for businesses, not only for storage and processing but also for operational requirements such as speed, maintaining performance with scalability, reliability, availability, security, and cost control; ultimately enabling them to maximize their profits by using the new possibilities offered by Big Data. In this article, we will explore and exploit the concepts and architectures of Big Data, in particular through the Hadoop open-source framework, and see how it meets the needs set out above, in its cluster structure, its components, its Lambda and Kappa architectures, and so on. We are also going to deploy Hadoop in a virtualized Linux environment, with several nodes, under the Oracle Virtual Box virtualization software, and use the experimental method to compare the processing time of the MapReduce algorithm on two DataSets with successively one, two, and three and four Datanodes, and thus observe the gains in processing time with the increase in the number of nodes in the cluster

Publisher

Politeknik Negeri Cilacap

Reference23 articles.

1. J. B. N. Penka, S. Mahmoudi, and O. Debauche, "A new Kappa Architecture for IoT Data Management in Smart Farming," in The 18th International Conference on Mobile Systems and Pervasive Computing (MobiSPC), Leuven, Belgium, Aug. 9-12, 2021, Procedia Computer Science, Sep. 2021.

2. G. K. Kalipe and R. K. Behera, "Big Data Architectures: A Detailed and Application Oriented Analysis," International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 9, Jul. 2019, ISSN: 2278-3075.

3. J. Lin, "The Lambda and the Kappa," University of Waterloo, Sep./Oct. 2017, IEEE Internet Computing.

4. Mr. H. Hashem, "Modélisation intégratrice du traitement BigData," Thèse de doctorat, Télécom SudParis, Ecole doctorale STIC, Université Paris-Saclay, Evry, France, Sep. 19, 2016.

5. A. Gillet, É. Leclercq, and N. Cullot, "Évolution et formalisation de la Lambda Architecture pour des analyses à hautes performances - Application aux données de Twitter," 2021 ISTE OpenScience, Published by ISTE Ltd., London, UK, openscience.fr.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3