At IBM Think 2018, Jason McGee showed how to modernize a decade-old Java web application called JPetStore into an AI-powered chatbot using nothing more than Docker, Kubernetes, and a few services from IBM Cloud. As a former Java developer who’s new to Kubernetes, I found it was not only a great introduction to emerging cloud technology but also a nostalgic look back at how far we’ve come.
While demos are a great overview, developers – myself included – will want to know more and get hands-on. To that end, we’ve obtained the demo’s code, cleaned it up a bit, and posted a tutorial on GitHub. We’ve also recreated the demo as a quick instructional video on YouTube with a few added enhancements.
Both the code and video will take you through the full modernization story. You’ll start by converting the tiers of the legacy J2EE stack into Docker containers. Then you’ll run and manage those containers using the IBM Cloud Kubernetes service. And finally, you’ll enhance the application with a chatbot microservice that uses Watson Visual Recognition and text messaging to create a new sales channel for the store.
In this PostgreSQL Tip, we demystify PostgreSQL template databases and how and when you might use them. Template databases are really useful when you use the same database objects every time you create a new database.
One key aspect of a robust architecture is that it is built to smoothly handle system failures, outages, and configuration changes without violating the data loss and consistency requirements of the use case. To proactively build such solutions needs an understanding of the possible exceptions and risky scenarios and preparedness to manage them efficiently.
To hit the ground running for any project based on cloud-based applications, it is very important to set up the associated infrastructure for the development of microservices. Quick and high-speed development can be achieved if the team has a way to do Continuous Integration (CI) and Continuous Delivery (CD) of the application.