International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Hazelcast Vs. Ignite: Opportunities for Java Programmers

  • Maxim, Bartkov (RooX Solutions Java Team Lead) ;
  • Tetiana, Katkova (University of Customs and Finance, Department of Innovative Technologies, Department of Cyber security) ;
  • S., Kruglyk Vladyslav (Bogdan Khmelnitsky Melitopol State Pedagogical University, Faculty of Informatics, Mathematics and Economics, Department of Informatics and Cybernetics) ;
  • G., Murtaziev Ernest (Bogdan Khmelnitsky Melitopol State Pedagogical University, Faculty of Informatics, Mathematics and Economics, Department of Mathematics and Physics) ;
  • V., Kotova Olha (Kherson State University, Faculty of Computer Science, Physics and Mathematics, Department of Algebra, Geometry and Mathematical Analysis)
  • 투고 : 2022.02.05
  • 발행 : 2022.02.28
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Abstract

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

Keywords

References

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