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!
Neil is a solution architect for Hazelcast®, the world's leading open source in-memory data grid. In more than 25 years of work in IT, Neil has designed, developed and debugged a number of software systems for companies large and small.
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An example demonstrating how a near-cache configuration option can be added to an existing application to improve performance. Performance increases, no coding is required. But it’s not a universally applicable solution, there are downsides to be aware of. What is a “near-cache“? Hazelcast provides a number of distributed storage containers, for storing data in the […]
Why? The normal deployment is for a JVM to contain a single Hazelcast instance, a client or a server. This means that the instance can utilise all the resources available to that JVM. In automated tests, it can frequently be useful to run multiple Hazelcast instances in the one JVM so that it is easy […]
A step-by-step example of how to introduce Hazelcast into an existing database backed application. The example here takes a Spring JPA example and augments this with Spring Data Hazelcast for added speed and resilience, without discarding what is already there. For a developer these are baby steps, but for an architect it’s giant leaps. Hola […]
In an earlier blog post, Caching Made Bootiful: The Hazelcast Way, Hazelcast’s Viktor Gamov demonstrated the ease of doing caching with Hazelcast in Spring. In this post, we’ll continue the theme to show how trivial session clustering is to implement from a coding perspective but also how this can radically change the application architecture for […]
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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.