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.
The distributed compute features of Hazelcast (such as Executors, EntryProcessors, and Jet pipelines) provide powerful capabilities for building distributed applications. By moving business logic into the in-memory data grid, movement of data across the network is minimized; this results in higher throughput and lower latency. Applications in this way can easily scale up and out given the built-in threading model and elastic scaling features of Hazelcast.
In this video, an application for fraud detection for credit card transactions is demonstrated, which is built on top of a reusable, generic rule engine implementation that is freely available online as an example implementation of several of Hazelcast’s key distributed computation capabilities.