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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The in-memory speed you count on, with the convenience and scalability of cloud.
In this webinar, we will present the tools that Hazelcast Jet brings to the table when it comes to operating long-running streaming applications in the cloud.
Through conceptual overviews, demos, and hands-on practice, you will learn to create stream processing pipelines using Java and Hazelcast Jet.
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Learn how the UK’s largest Internet of Things network, British Gas, uses Hazelcast® in-memory data grid to serve over 200,000 homes with a system that allows users to remotely control their heating and hot water temperature from their mobile device or on the Web. British Gas uses Hazelcast IMDG® to store large amounts of data in memory for quick access and integrate that memory store with its current core back-end platform in order to linearly scale to the growing needs of its network.
Event stream processing continues to play an increasingly important role in today’s data architectures. This is no surprise, considering that companies are striving to respond faster to ongoing changes in their business environments. However, these companies are still not taking full advantage of the value of their data, typically because they have not planned for the right approaches and architectures for stream processing. Read this Gartner report to learn more.
Learn how Ellie Mae, a leading provider of innovative on-demand software solutions and services for the residential mortgage industry, found a solution in Hazelcast IMDG that enabled them to achieve horizontal scale while continuing to deliver on service level agreements (SLA), allowing them to effectively service larger and more demanding customers.
This case study profiles a major US media company's use of in-memory technology to enable high-volume Machine Learning and Artificial Intelligence applications used to build out 360-degree customer profiles, in real-time, that are used to create chatbots to handle customer support inquiries.
This global pizza delivery chain operates in 82 countries, making it the second-largest franchised pizza chain in the world. Today, it operates more than 12,600 pizza restaurants around the world and delivers more than 1 million pizzas each day.
Gamesys, the award-winning online gaming company, required a highly scalable poker platform that could be used for both real money and social gaming. The challenge was to build a cost-effective platform that could manage failures without losing a player’s money, was highly available and elastic and could handle tens of thousands of concurrent players.
Stream processing is the new critical application for today’s always-on, always available digital ecosystem. While stream processing can cover a wide range of applications (healthcare, IoT, payment processing, etc.), Hazelcast’s stream processing engine - Jet - has had an interesting and unusual deployment in the oil and gas drilling domain, with spectacular results.
In this guide, you will learn how to use Hazelcast distributed caching with Spring Boot and deploy to a local Kubernetes cluster.
In this guide, you will learn how to use Hazelcast distributed caching with MicroProfile and deploy to a local Kubernetes cluster. You will then create a Kubernetes Service which load balances between containers and verify that you can share data between microservices. The microservice you will deploy is called hazelcast-microprofile. The hazelcast-microprofile microservice simply helps […]
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