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!
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The in-memory speed you count on, with the convenience and scalability of cloud.
Store hundreds of gigabytes of data in-memory without garbage collection overhead, operating within consistent and predictable latencies.
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.
Payment processing systems require extremely high throughput rates as well as millisecond-level latencies. By leveraging the Hazelcast In-Memory Computing Platform, businesses gain a significant performance advantage to successfully process high volumes of transactions. Hazelcast also provides the scalability to easily grow and adapt to changing transaction volumes, which is especially important during heavy purchasing seasons and events with loads that spike well beyond typical levels.
In this white paper, Hazelcast reviews the current state of the US payments markets, including the primary business and technology drivers, and the overarching role that fraud detection plays in ensuring a safe and stable experience for consumers, business, and the network providers.
Part of deploying Jet is to determine a good estimate of the number of computing resources you need to optimally run your Jet application(s). This guide will walk you through specific environments as examples which can then be extrapolated for your own specific workloads.
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.
