A New Architecture for Real Time Data Stream Processing

Abstract

Processing a data stream in real time is a crucial issue for several applications, however processing a large amount of data from different sources, such as sensor networks, web traffic, social media, video streams and other sources, represents a huge challenge. The main problem is that the big data system is based on Hadoop technology, especially MapReduce for processing. This latter is a high scalability and fault tolerant framework. It also processes a large amount of data in batches and provides perception blast insight of older data, but it can only process a limited set of data. MapReduce is not appropriate for real time stream processing, and is very important to process data the moment they arrive at a fast response and a good decision making. Ergo the need for a new architecture that allows real-time data processing with high speed along with low latency. The major aim of the paper at hand is to give a clear survey of the different open sources technologies that exist for real-time data stream processing including their system architectures. We shall also provide a brand new architecture which is mainly based on previous comparisons of real-time processing powered with machine learning and storm technology.

Authors and Affiliations

Soumaya Ounacer, Mohamed Amine TALHAOUI, Soufiane Ardchir, Abderrahmane Daif, Mohamed Azouazi

Keywords

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  • EP ID EP240422
  • DOI 10.14569/IJACSA.2017.081106
  • Views 108
  • Downloads 0

How To Cite

Soumaya Ounacer, Mohamed Amine TALHAOUI, Soufiane Ardchir, Abderrahmane Daif, Mohamed Azouazi (2017). A New Architecture for Real Time Data Stream Processing. International Journal of Advanced Computer Science & Applications, 8(11), 44-51. https://europub.co.uk./articles/-A-240422