Developing Big Data in Computing Applications with Lambda Architecture

Authors

  • M. Mohammed Thaha Assistant Professor (Sr.Grade), B.S.Abdur Rahman Crescent Institute of Science and Technology, GST Road, Vandalur, Chennai - 600 048, Tamilnadu, INDIA.
  • Aizul Nahar Harun YU-MJIIT International Joint Intellectual Property Lab (YU- MJIIT IJIPL), Department of Management of Technology, Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
  • Rafikullah Deraman Project and Facilities Management Research Group, Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia.
  • T. Jackulin Associate Professor, Panimalar Engineering College, Chennai, India
  • Vignesh. T. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fileds, Vaddeswaram

Keywords:

Big Data, Computing Applications, Lambda Architecture, empirical validation, dominance

Abstract

The IT industry and academics have both paid close attention to big data. Information generation and collection in the digital and computing worlds happen at a rate that quickly surpasses the limit. Globally, there are currently over 5 billion mobile phone owners and over 2 billion Internet users. There are projected to be 50 billion Internet-connected gadgets by 2020. At this point, data generation is predicted to increase 44 times over that of 2009. This application is distinct from hardware-centric evaluations in that it employs real-world data-intake scenarios to demonstrate the methodology's effectiveness. This work adds to the corpus of prior research on LA while simultaneously addressing a significant gap in the field. This work establishes a standard for further research in this field by providing a fresh, empirically validated technique for assessing LA, a methodology that may be used in various big-data architectures. It also advances past work that lacked empirical validation. The field's future research orientations are defined by the prospects and various unresolved difficulties in Big Data dominance. It is simpler to research the area and develop the most effective techniques for handling Big Data thanks to these lines of investigation.

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References

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Published

24.11.2023

How to Cite

Thaha, M. M. ., Harun, A. N. ., Deraman, R. ., Jackulin, T. ., & T., V. . (2023). Developing Big Data in Computing Applications with Lambda Architecture. International Journal of Intelligent Systems and Applications in Engineering, 12(5s), 460–468. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3931

Issue

Vol. 12 No. 5s (2024)

Section

Research Article

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