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by Anton McConville, Olaph Wagoner | Published August 1, 2019
This code pattern shows how Kubernetes-based microservices modernize a traditional application, demonstrates steps to deploy an app on OpenShift Source-to-Image toolkit, and explores open standards, front-end technologies for custom charts and responsive design.
Imagine a fictional, conceptual healthcare or health insurance company that has been around for a long time. It has hundreds of thousands of patient records in an SQL database connected to a either a mainframe, or a monolithic Java back end. Our sample code shows health records that look very similar to the health records of most insurance companies.
Here’s a view clients might see when they log in:
The healthcare company has recently started understanding how machine learning on some of the patient records might surface interesting insights. Machine learning is a big interest and popular topic of discussion among large data companies.
Our example healthcare company has also heard a lot about cloud computing. The company has a lot of traditional code in the mainframe, and it works well for now… but the company’s leaders think it would be a complimentary opportunity to explore some machine learning in the cloud (either public or private).
The business rules for the system are written in either COBOL or Java. It includes some entitlement rules, prescription rules, coverage rules coded in that system.
In this code pattern, you learn the following skills:
This project stands alone in test mode, or integrates with associated projects.
Ready to get started? Find detailed technical steps for this code pattern in the README.md file in the GitHub repository.
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Review the history of the enterprise Kubernetes application OpenShift and the intertwined paths with IBM Cloud Kubernetes service.
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