Hazelcast Cloud is an enterprise-grade in-memory computing platform deployed and managed by the Hazelcast CloudOps team. The service
is powered by Hazelcast IMDG Enterprise HD and leverages widely adopted technologies, such as Docker and Kubernetes, to provide dynamic orchestration and containerization. Hazelcast Cloud supports applications developed in some of the most common languages, including Java, Node.js, Python. Go, and .NET.
Hazelcast Cloud delivers enterprise-grade Hazelcast software in the cloud, deployed as a fully managed service. Leveraging over a decade of experience and best practices, Hazelcast Cloud delivers a high-throughput, low-latency service that scales to your needs while remaining simple to deploy. If you’re considering moving to the Cloud, or are looking for an easy ramp on deploying in-memory technology, this white paper on migrating in-memory to the cloud is an informative and helpful resource.
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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We are excited to announce the new repository for extension modules of Hazelcast Jet. This repository will be home for the Hazelcast Jet extension modules, including connectors (including both sources and sinks), custom aggregations, and context factories. All of these modules will help make the integration easier for 3rd-party products in your pipelines. The main […]
Stream processing refers to real-time management of data entering a banking system (or any information system, actually) at high speed and volume, usually from a broad range of sources. The “management” aspect means data is wholly or partially processed and contextualized before entering an in-memory (operational) system, where pre-processing can significantly accelerate response times. Before […]
After releasing Hazelcast Jet 3.0 in May, we are happy to announce its first update, Hazelcast Jet 3.1. Hazelcast Jet is now an Apache Beam Runner Apache Beam is a framework for building distributed batch and stream processing applications over a unified API. The API itself is decoupled from the underlying execution implementation, making it […]
Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business – IoT sensor network data streams, mobile usage statistics, large scale monitoring, the list is endless. Numerous applications seek the ability to quickly react to dynamic streaming data, as it is either a mandatory requirement or a competitive advantage. API Churn As a consequence, lots […]
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 […]
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Artificial Intelligence (AI) as a concept has been around since the development of computational devices, as early as the creation of Turing machines during World War II. The term itself was first coined by University of Washington professor John McCarthy in 1956, and now, 60+ years later we see the actual commercialization of AI. Why […]
On Monday, Speedment and Hazelcast announced a partnership focused on accelerating application development when deploying an in-memory data grid alongside legacy databases. The following post was written by Speedment’s CTO, Per-Åke Minborg. What if you want to use a Hazelcast In-Memory Data Grid (IMDG) to speed up your database applications, but you have hundreds of […]
This is an example showing one way you might connect to Hazelcast Cloud, and how you can harness its power for improved performance. As a problem, we’re going to calculate the spread of customer satisfaction, as the average doesn’t give enough insight. The example shows a custom domain model and server-side code execution. It shows […]
Due to its underlying architecture and many years of optimization, Hazelcast is extremely fast and dramatically outperforms Redis Labs (and Redis open source), especially at scale. Last year, Redis Labs published a very misleading benchmark against Hazelcast. We have closely investigated Redis Labs’ test and discovered many misleading aspects of the benchmark. As a result, […]
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 […]
Another massive, transformative technology wave is about to hit, and while this won’t be quite as overt as some of the other waves (e.g., mobility, social media), its effect will be much more pervasive and will hit every consumer, business and industry on multiple levels. The next generation of mobile infrastructure, 5G, is in the […]
Hazelcast Jet is known for its high-performance and this is partially due to it employing green threads. This approach avoids the need for operating system threads, which are expensive to switch. Instead, task switching is performed by the application code in a lightweight way. What are green threads? With “normal” threads, you ask the OS […]
We are happy to announce the first General Availability release of Hazelcast Jet, two years after our first public release and three years after the project first started. We named this version 3.0 to match the current major version of Hazelcast IMDG, which is the underlying technology used in Jet. This version also includes several […]
We are pleased to announce the production-ready release of Hazelcast IMDG 3.12. We’ve crushed a lot of bugs, provided general performance improvements, plus we’ve added some great new features (more of which below). The release by numbers: 676 Issues 784 Pull Requests 47 Committers 168 Days Elapsed CP Subsystem The new CP Subsystem provides implementations […]
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