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.
The topic of microservices continues to get significant buzz as businesses build more and more complex solutions. There are many advantages to microservices, and this paper aggregates them into six buckets to discuss why a microservices architecture can work well for you.
This white paper discusses the value of cloud deployments and how Hazelcast Cloud Enterprise can help you get the most out of your investment in cloud applications.
Hazelcast Cloud is an on-demand managed service for the Hazelcast In-Memory Data Grid.
While a microservices architecture is more scalable than a monolith, it has a direct hit on performance. To cope with that, one performance improvement is to set up a cache. It can be configured for database access, for REST calls or just to store session state across a cluster of server nodes. In this demo-based talk, I’ll show how Hazelcast In-Memory Data Grid can help you in each one of those areas and how to configure it.
In this talk, Marko will show one approach which allows you to write a low-latency, auto-parallelized and distributed stream processing pipeline in Java that seamlessly integrates with a data scientist's work taken in almost unchanged form from their Python development environment. The talk includes a live demo using the command line and going through some Python and Java code snippets.
Having fault-tolerance can be a factor to choose a distributed system even if the expected load can be handled by a single machine - a distributed system can tolerate failures of its parts while a system running on a single machine cannot. How can a stream-processing engine guarantee an exactly-once semantics? Viliam will describe the Chandy-Lamport algorithm that can be used to consistently snapshot the global state of a distributed system. I’ll also describe its special simplified case that’s used in Jet.
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