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    A comparative performance of real-time big data analytic architectures
    (2019-07-01)
    Sanla, Apisit
    ;
    Numnonda, Thanisa
    Nowadays, many organizations pay attention to the relevant technologies of Big Data to analyze more accurately, quickly, and efficiently. Real-time Big Data analytics is challenging due to the massive volume of complex data needed to distribute in processing. Therefore, in this research, we investigate two state-of-the-art architectures: Lambda and Kappa. The Kappa architecture is simply the Lambda architecture without the batch layer. To help businesses decide on the right architecture, their processing time, and resource utilization in the same environment need to be found out. Experiments had been carried out with the data size 3 MB, 30 MB, and 300 MB. The results showed that Lambda architecture outperforms Kappa architecture around 9% for the accuracy test when using processing time approximately 2.2 times more than Kappa architecture. Lambda architecture also used more 10-20% of CPU usage and 0.5 GB of RAM usage than Kappa architecture.
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    A Comparative Study on Performance and Resource Utilization of Real-time Distributed Messaging Systems for Big Data
    (2019-07-01)
    Intorruk, Somprasong
    ;
    Numnonda, Thanisa
    In the past few years, data continuously increase and in various forms called Big Data. Besides, data analytics play an important role more and more in most organizations. From these reasons, many efficiently distributed messaging systems have been introducing to handle Big Data in real-time. However, choosing the appropriate and efficient methods and tools to transfer Big Data is still challenging. Therefore, this paper purposes of comparing the architecture, performance, and resource utilization between Apache Kafka which is one of the favorite tools for Big Data and Apache Pulsar which is similar to Kafka and become one of the latest tools for big data. After we implemented both systems in the same environment, the results show that Pulsar outperforms in throughput, latency, and average resource utilization especially when the size of messages is small (such as 1 KB and 1MB).