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.
Click to copy, then paste the following snippets into your build:
<repository> <id>private-repository</id> <name>Hazelcast Private Repository</name> <url>https://repository.hazelcast.com/release/</url> </repository> <dependency> <groupId>com.hazelcast.jet</groupId> <artifactId>hazelcast-jet-enterprise</artifactId> <version>4.3</version> </dependency>
compile 'com.hazelcast.jet:hazelcast-jet-enterprise:4.3'
Pull hazelcast docker image from Docker registry via this command:
docker pull hazelcast/hazelcast-jet-enterprise
After that you should be able to run Hazelcast docker image by:
docker run -e JET_LICENSE_KEY=<your_license_key> -ti hazelcast/hazelcast-jet-enterprise
Install Hazelcast from the official Helm Chart via this command:
helm repo add hazelcast https://hazelcast.github.io/charts/ helm repo update helm install --set image.tag=4.3 hazelcast/hazelcast-jet-enterprise
Hazelcast IMDG clients and programming language APIs allow you to extend the benefits of operational in-memory computing to applications in these languages. All of these clients and APIs are open source and all except the Scala client are supported by Hazelcast.
