This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
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.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
With our recent release of Hazelcast IMDG 4.0, we would like to invite you to watch this video, where we will discuss the new features in this release at a high level and talk about how you can take advantage of them.
Hazelcast Cloud Enterprise is the new cloud-native managed service that allows you to quickly set up Hazelcast IMDG in a public cloud, fully managed for you by Hazelcast. This tutorial will walk through deployment of Hazelcast Cloud Enterprise on Amazon AWS.
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Looking for info on our live events? We're busy coordinating developer events at the moment, so please check back in a few days for the latest info. In the meantime, check out our free, on-demand training.
Machine learning (ML) brings exciting new opportunities, but applying the technology in production workloads has been cumbersome, time consuming, and error prone. In parallel, data generation patterns have evolved, generating streams of discrete events that require high-speed processing at extremely low response latencies. Enabling these capabilities requires a scalable application of high-performance stream processing, distributed application of ML technology, and dynamically scalable hardware resources.
See how the distributed compute features of Hazelcast can be used to build a rule engine for low-latency, high-throughput transaction processing.
Mainframe computers are used at many companies today, but the need for more cost-effectiveness is forcing changes. A popular strategy, mainframe optimization, enables lower mainframe costs due to the reduction in unnecessary MIPS. At the same time, it adds powerful new architectures related to cloud, microservices, and data streaming. An integration with IBM and Hazelcast […]
In this webinar, we’ll show how the microservices model can fall short in addressing the need for distributed access to shared data, and how Hazelcast can be used in a microservice architecture to solve some of the most difficult challenges facing developers and architects in building a robust distributed data architecture.
The first minor release in the new 4.x product line of Hazelcast IMDG brings not only new major features such as initial preview of SQL support for querying and generic object interface for domain objects, but also many usability improvements to speed up development with focus on Cloud environment.
Read why you should use Hazelcast over the Red Hat Data Grid for your application acceleration and architecture modernization initiatives.
BNP Paribas S.A. is a French international banking group and one of the largest banks in the world by total assets.
Sr. engineer Grzegorz Piowawarek walks through the integration of Hazelcast as a Hibernate second level cache within Spring Boot
Sr. engineer Grzegorz Piowawarek walks through the integration of Hazelcast into a Quarkus application.
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