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Real-Time Big Data Analytics for Data Stream Challenges: An Overview

Author

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  • Alaa Abdelraheem Hassan

    (Khartoum University, Sudan)

  • Tarig Mohammed Hassan

    (Khartoum University, Sudan)

Abstract

The conventional approach of evaluating massive data is inappropriate for real-time analysis; therefore, analysing big data in a data stream remains a critical issue for numerous applications. It is critical in real-time big data analytics to process data at the point where they are arriving at a quick reaction and good decision making, necessitating the development of a novel architecture that allows for real-time processing at high speed and low latency. Processing and anlayzing a data stream in real-time is critical for a variety of applications; however, handling a large amount of data from a variety of sources, such as sensor networks, web traffic, social media, video streams, and other sources, is a considerable difficulty. The main goal of this paper is to give an overview of the current architecture for real time big data analytics, real-time data stream processing methods available, including their system architectures Lambda, kappa, and delta large data stream processing.

Suggested Citation

Handle: RePEc:epw:comput:v:2:y:2022:i:4:id:10062
DOI: 10.24018/compute.2022.2.4.62
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