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.
Whether the restart is a planned shutdown or a sudden cluster-wide crash, Hot Restart Store allows full recovery to the previous state of configuration and cluster data.
Each node controls its own local snapshot, providing linear scaling across the cluster.
Hot Restart Store supports the IMap and JCache interfaces, as well as Web Sessions and Hibernate, with further data structures planned in subsequent releases.
Architecture and Features
Persistence store optimized for SSD and mirrored in native memory.
Each node operates its own independent store.
Data entirely loaded into RAM on reload, ensuring you always operate at in-memory speeds.
Configurable per data structure for JCache, Map, Web Sessions and Hibernate.
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.