A Comparative Performance of Real-time Big Data Analytic Architectures

Abstract

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.