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COVID-19 data analytics with Kubernetes and OpenShift, Part 4

Introduction

A range of data is published on the impact of various parameters on the spread of COVID-19, including population density, average number of people per household, ethnicity, and weather data. Have you ever wanted to run your own analytics on COVID-19 data and examine data sets to draw a particular conclusion? Or possibly evaluate a theory that might or might not be true? Such analytics can potentially shed light on the impacts of various factors, and you can apply them to a variety of problems. Maybe you would like to see the impact of temperature and humidity on the spread of COVID-19 in different countries?

This workshop is part of a multipart workshop series on building, deploying, and managing microservices applications with Kubernetes and Red Hat OpenShift. The series of seven hands-on workshops focuses on COVID-19 data retrieval, parsing, and analytics. The series explains how you can retrieve COVID-19 data from an authentic source and make it securely available through REST APIs on Kubernetes and OpenShift. The primary applications are developed in the Spring Boot open source Java-based framework, but you add more features and apply analytical services on the data in the form of microservices written in different programming languages.

In this fourth workshop of the COVID-19 data analytics with Kubernetes and OpenShift series, you dive into the Red Hat OpenShift Container Platform and experience how OpenShift simplifies and secures your orchestration tasks by automating the steps taken with Kubernetes. You first use the command-line interface tool to deploy and scale the built containers. Then, you use the OpenShift web console to deploy the application by using only its source code with a few clicks. That powerful feature for developers is called Source-to-Image (S2I).