Companies need a data-processing solution that increases the speed of business agility, not one that is complicated by too many technology requirements. This requires a system that delivers continuous/real-time data-processing capabilities for the new business reality.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing?
In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Setting up servers and configuring software can get in the way of the problems you are trying to solve. With Hazelcast Cloud we take all of those pain points away.
Watch this webinar to learn how you can instantly fire up and then work with Hazelcast Cloud from anywhere in the world. With our auto-generated client stubs for Java, Go, Node.js, Python and .NET, we can have you connected and coding in less than a minute!
In this guide, you will learn how to use Hazelcast distributed caching with Spring Boot and deploy to a local Kubernetes cluster. You will then create a Kubernetes Service which load balances between containers and verify that you can share data between microservices.
The microservice you will deploy is called hazelcast-spring. The hazelcast-spring microservice simply helps you put data and read it back. The Kubernetes Service will send the request to a different pod each time you initiate the request, and the data will be served by a shared hazelcast cluster between hazelcast-spring pods.
You will use a local single-node Kubernetes cluster. However, you can deploy this application on any Kubernetes distributions.