This in-depth look at how to Spring Boot Docker images to the Amazon EC2 Container Registry provides a good example of Spring, Java, the cloud, and container usage.
Failing fast isn't unique to DevOps but it's an incredibly important strategy. Practicing failures and mitigating resulting issues is a standard function of DevOps teams.
It's tempting to provision more memory to your VM than you need, but that can cause headaches—and containers might make it worse. The answer lies in auto-scaling.
Microservies and Docker have become the peanut butter and jelly of modern app delivery. They allow organizations to work in a consistent, isolated runtime environment.
Docker Swarm makes it relatively easy to scale apps. With the help of Terraform and Packer, you can set up scaling for an app using cloud-native infrastructure.
DevOps one-stop shop solutions can slow you down over time. Tools that equal a stage to an environment deployment miss out on the real power of deployment pipelines.
Sibanjan Das offers up a tutorial for building a web-based cluster and prediction analysis application through using R with the open source Shiny framework. Oh yeah, and he embedded the app directly into this DZone article... shine on you crazy data scientist.
In Extreme Programming, instead of delivering everything you could possibly want on some date far in the future, you deliver the software you need as you need it.
If you have Redis, Node.js, and the Heroku toolbelt installed on your machine, then you've got everything you need to build a real-time chat application.
Using a poor-quality server wastes everyone's time because the build takes too long to finish, resulting in intermittent test results and frustrated engineers.
Let's look into the Apache Ignite Cluster Layer, a GitHub project that includes the basic building blocks needed to implement a proposed microservices-based architecture.
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