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.
Job Upgrade makes use of Jet state snapshots to address a variety of requirements, including modifications in business logic, bug fixes, and configuration changes.
Easy to Implement
Job upgrades can be triggered via a Java API or from a command line.
State snapshots of current jobs are taken and saved.
New classes or changes are distributed to the Jet nodes.
New versions are started, and data is read from the saved snapshot.
No Disruption to Customers
Everything listed above happens in milliseconds, with essentially no latency.
Allows A/B Testing on New Features
Multiple versions of a Jet Job can run concurrently, writing results to separated sinks.
After the testing period, pick the better version, and shut down the other.
Introduce relevant filtering or transforming steps to streaming data-centric applications.
Change business rules or add new operators as the business logic driving your streaming applications evolves.
Change IP addresses for sources or sinks without disrupting streaming services.
This video by Hazelcast senior solutions architect Sharath Sahadevan walks through a setup of WAN Replication on Google Cloud Platform.
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.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.