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.
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.
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).
In this episode of HazelVision, we cover the basics of installing Hazelcast IMDG using common package managers, starting up a cluster via the Command Line Interface, and writing a simple client program to access the cluster.
Hazelcast was evaluated as a Strong Performer in this report, and received the top scores possible in the enrichment, throughput, latency and availability criteria. Hazelcast also earned a 3 out of 5 score in the sequencing, aggregates, extensibility, usability, options for deployment, and four other criteria.
In the modern world what makes the difference is the shelf-life of your data analysis. When you run analysis on your data to derive insights, these insights rely on the recency of the data. All you need is a stream processing engine integrated with a fast data store. Changes in core data in the data store flow through streaming analytics to create derived data. If it’s all integrated, performance is excellent for high volume and low latency.
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.