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.
In the modern world what makes the difference is the shelf-life of your data analysis. When you run analysis on your data to derive insights, these insights rely on the recency of the data. All you need is a stream processing engine integrated with a fast data store. Changes in core data in the data store flow through streaming analytics to create derived data. If it’s all integrated, performance is excellent for high volume and low latency.
A global bank rolled out a highly scalable, cross-border payment system.
Understanding driver behavior via the use of connected cars can help organizations make data-driven decisions to reduce safety risks, improve commercial driver productivity, and streamline fleet operations. In this webinar by Hazelcast and Intel, we will show a method for the non-intrusive and real-time detection of visual distraction.
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.
