<![CDATA[Increasing the number of Internet of Things devices every day causes an increase in the volume of data that has no benefit if not done data processing. The most significant challenges in data processing are resource requirements, complex installation processes, difficulties in using analytical tools, and complex maintenance and development processes. This research tries to solve this problem by developing a heart rate data analysis infrastructure using microservices architecture, Docker containers, and Kubernetes. The use of microservices architecture aims to facilitate the development and maintenance of applications. The use of a docker to simplify the installation and deployment process by changing applications with microservices architecture to a docker image. Kubernetes is used to carry out the management of the container docker that runs on the Kubernetes cluster. Scalability testing shows that the infrastructure built is capable of analyzing up to 100,000 data. However, when analyzing 50,000 data there was a drastic increase in an analysis time of seven to eight times. In addition to analytical skills, this study also produces a heart rate data analysis infrastructure that can perform self-healing and can store data persistently.]]>
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