Easily scale enterprise applications using distributed data grids
I want to show you the variety of options you get when you design applications around distributed data grids. Building scalable applications is often not an easy task. But with the right tools, it is much easier than it seems. Distributed in-memory data grids provide a backbone for building horizontally scalable applications, while at the same time they provide flexible caching to scale up the performance vertically. Suddenly it will be possible to tweak the applications beyond what you would expect, with very little effort, often without even rebuilding the applications. We’ll analyze what’s possible and how to do it, not only in theory but also demonstrating on an application based on Java EE, Hazelcast, and Node.js. In the end, you’ll understand the power of distributed data grids and how to use them efficiently to scale the applications in various scenarios, be it high-throughput, low-latency, microservice architecture and more.
Ondrej is a software developer and consultant specializing in combining standard and proven tools to solve new and challenging problems. He's been developing in Java and Java EE for 9 years. As a Scrum Master and experienced Java EE developer, he's helped companies to build and educate their development teams, improve their development processes and be flexible and successful in meeting client requirements. He loves working with Java EE community and would welcome anyone to contribute to Payara, as well as to any other opensource project in the Java EE ecosystem.