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.
Overview
Hazelcast Jet’s Lossless Recovery feature provides advanced fault-tolerance support for high-volume, mission-critical applications
Automatic Replication and Persistence of Critical Data
Data is continuously snapshotted and backed up to the disk, allowing the seamless recovery from the cluster-wide failures and shutdowns.
Uninterrupted Performance for Customer-Facing Streaming Data
Snapshotting, replication and persisting automatically occur in milliseconds, with essentially no noticeable effect on user experience.
Features
Jet stores data in multiple replicas (copies) across the cluster, which are recovered from the back-up if a node fails.
Continuously persists the states of the cluster members on disk in a format specifically designed for restart performance and to work in concert with SSDs.
Consistent snapshots of each job are saved to storage, configured to be persistent with Hazelcast Hot Restart.
Rewindable sources are rewound using offsets saved in the snapshot (Kafka, Hazelcast IMap, Hazelcast ICache data sources support rewinding).
Resources
Kubernetes brings new ideas on how to improve the performance of your microservices. You can use a cache or a distributed in-memory store and set them up with several different topologies: embedded, embedded distributed, client-server, cloud, sidecar, reverse proxy, and reverse-proxy sidecar. In this session you’ll see: A walk-through of all topologies for in-memory storage […]
This whitepaper discusses how an in-memory computing platform is used in the healthcare industry to help improve patient care.
Learn how the cloud-native architecture of Hazelcast works with Kubernetes when deploying fast cloud applications.
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.
