Create a health data analytics app with legacy mainframe code and the cloud
Build a Node.js analytics web application that connects to a zOS Mainframe
Example Health, a fictional healthcare and insurance company, has a Node.js-based analytics web application for a health records system. The web application is designed to showcase the best in class integration of modern cloud technology in collaboration with legacy mainframe code.
Example Health’s premise is that it has been around for a long time and has several hundreds of thousands in patient records on an SQL database connected to a z/OS mainframe. Example Health’s records look very similar to the health records of most insurance companies that exist today.
Example Health has recently started to understand how data science and analytics on some of the patient records might surface interesting insights. Example Health has also heard a lot about cloud computing and would like to implement app modernization. There is a lot of legacy code in the mainframe and it works well for now, but Example Health thinks it may be a timely opportunity to explore some data science and analytics in the cloud.
Using Cloud Foundry
- The Data Service API acts as a data pipeline and is triggered to update the data lake with updated health records by calling API Connect APIs associated with the z/OS Mainframe.
- APIs from API Connect process relevant health records data from z/OS Mainframe data warehouse and send the data through the data pipeline.
- The Data Service data pipeline processes the z/OS Mainframe data and updates the MongoDB data lake.
- Users interact with the UI to view and analyze analytics.
- When the user interacts with the app, the UI is handled by Node.js where the API calls are initialized.
- The API calls are processed in the Node.js data service and are handled accordingly.
- The data is gathered from the MongoDB data lake from the API calls.
- The responses from the API calls are handled by the application’s UI.
Ready to get started? Find the detailed instructions in the README file.
- Get a Mapbox Access Token to make the API calls.
- Run the application using data on the zOS Mainframe or by generating data.
- Deploy to the cloud using either Kubernetes or Cloud Foundry.