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.
Use containerized in-memory technology to accelerate cloud applications. This video shows how to set up Hazelcast in AWS ECS.
Adding a fast, scalable, distributed cache to your Spring Boot applications takes very little effort. Watch this video to see how.
Credorax is a next-generation smart payments provider and fully licensed acquiring bank providing cross-border processing.
Swedbank is a large banking group based in Stockholm that uses in-memory computing to speed up systems driving mobile apps.
This paper discusses the role of machine learning in fraud detection, and why improved fraud detection models are required today.
The business use case we'll use for this demonstration is a Trade Monitoring application for middle-office and back-office teams in a capital markets trading firm.
Back office analysts at capital market trading firms can now get on-demand, near-real-time summaries of the day’s trades.
The “cost versus risk” balance in capital markets trading firms can now be more efficiently addressed with modern technologies.
Analysts in the back office of capital markets trading firms need greater visibility on trades throughout the day. This reference architecture paper describes the use of the Hazelcast In-Memory Computing Platform to cost-effectively enable a near-real-time stock trading analysis solution.
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