This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
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.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
In this guide, you will learn how to use Hazelcast distributed caching with MicroProfile 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-microprofile. The hazelcast-microprofile microservice simply helps you write (“put”) data and read it back. As the Kubernetes Service sends the request to a different pod each time you initiate the request, the data will be served by the shared hazelcast cluster between hazelcast-microprofile pods.
You will use a local single-node Kubernetes cluster. However, you can deploy this application on any Kubernetes distribution.