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!
Rafał is a passionate software engineer, trainer, conference speaker, and author of the book, Continuous Delivery with Docker and Jenkins. He specializes in Java development, cloud environments, and continuous delivery. Prior to joining Hazelcast, Rafał worked with a variety of companies and scientific organizations, including Google, CERN, and AGH University of Science and Technology.
No posts were found matching that criteria.
Hazelcast loves Kubernetes. Thanks to the dedicated Hazelcast Kubernetes plugin, you can use dynamic auto-discovery. Hazelcast on Kubernetes can also run in multiple topologies: embedded, client-server, or as a sidecar. What’s more, thanks to the Helm package manager and the dedicated Hazelcast Helm Chart, you can deploy a fully functional Hazelcast server in literary minutes. […]
Data is valuable. Or I should write, some data is valuable. You may think that if the data is important to you, then you must store it in the persistent volume, like a database or filesystem. This sentence is obviously true. However, there are many use cases in which you don’t want to sacrifice the […]
I’m sure you use caching somewhere in your system. This can be either to improve performance, reduce backend load, or to decrease downtime. Everybody uses caching. Caching is everywhere. However, in which part of your system should it be placed? If you look at the following diagram representing a simple microservice architecture, where would you […]
Hazelcast IMDG is tightly integrated into the Kubernetes ecosystem thanks to the Hazelcast Kubernetes plugin. In previous blog posts, we shared how to use auto-discovery for the embedded Hazelcast and steps for scaling it up and down using native kubectl commands. In this post, we’ll focus on another useful feature, Rolling Upgrade. You can apply […]
The sidecar pattern is a technique of attaching an additional container to the main parent container so that both would share the same lifecycle and the same resources. You may think of it as a perfect tool for decomposing your application into reusable modules, in which each part is written in a different technology or […]
Hazelcast IMDG supports auto-discovery for many different environments. Since we introduced the generic discovery SPI, a lot of plugins were developed so you can use Hazelcast seamlessly on Kubernetes, AWS, Azure, GCP, and more. Should you need a custom plugin, you are also able to create your own. If your infrastructure is not based on […]
Hazelcast IMDG is a perfect fit for your (micro)services running on Kubernetes since it can be used in the embedded mode and therefore scale in and out together with your service replicas. This blog post presents a step-by-step description of how to embed Hazelcast into a Spring Boot application and deploy it in the Kubernetes […]
Hazelcast IMDG can be fairly simply configured to work on AWS ECS. This Blog Post presents this process step by step.
Hazelcast is well integrated with the Kubernetes environment. Using Hazelcast Kubernetes Plugin, Hazelcast members discover themselves automatically. Using Hazelcast Helm Charts, you can deploy a fully functional Hazelcast cluster with a single command. Now, it's time to focus on the operational part and describe what to do if you want to scale up or down the number of Hazelcast members in a cluster.
There are no more posts.
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.