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.
Overview
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.
High-Density Architecture
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.
Architecture
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.
High performance in your microservices is often required, but not always easy to achieve. To get the most out of your microservices deployments, you need to make sure you have the right technologies in place. We'll show how the integration of distributed in-memory data platforms can support the elasticity, statefulness, and performance of advanced microservices and examples of microservices patterns using a popular open-source project.
In this episode, we show how Hazelcast data is partitioned to provide benefits such as greater scalability and concurrency.
Neil Stevenson provides a demo of the Hazelcast Platform in action.
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.
