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!
Store hundreds of gigabytes of data in-memory without garbage collection overhead, operating within consistent and predictable latencies.
Storing hundreds of gigabytes of data in-memory lowers cost by reducing the number of required CPUs and nodes. It also increases uptime through a reduction of garbage collection overhead, and accelerates application performance.
High-Density Memory Store
Store hundreds of gigabytes of data without garbage collection overhead.
Operate within consistent and predictable latencies.
Scale Up then Scale Out
Fully use the resources of your machine then scale out for redundancy and capacity.
Create a more stable and predictable cluster.
Use separate on-heap memory store and high-density memory store.
Hold data in your Java virtual machine (JVM) process while avoiding garbage collection pauses.
Hazelcast High-Density Memory Store solves garbage collection limitations so that applications can exploit hardware memory more efficiently.
Scale-up then scale-out by using a smaller number of large computers, creating a more stable and predictable cluster.
Hold data in your Java virtual machine (JVM) processes while avoiding garbage collection pauses.
Your entire data sets can now be held in cache for extreme in-memory performance, all with the simplicity of JCache or Map.
In this video tutorial, Hazelcast cloud software engineer Rafal Leszko walks you through the steps to get Hazelcast running in embedded mode in a Kubernetes cluster.
Get up and running with the Hazelcast IMDG C# / .NET Client quickly with this easy to use reference card.
Get up and running with the Hazelcast IMDG Python Client quickly with this easy to use reference card.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.