Companies need a data-processing solution that increases the speed of business agility, not one that is complicated by too many technology requirements. This requires a system that delivers continuous/real-time data-processing capabilities for the new business reality.
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.
Setting up servers and configuring software can get in the way of the problems you are trying to solve. With Hazelcast Cloud we take all of those pain points away.
Watch this webinar to learn how you can instantly fire up and then work with Hazelcast Cloud from anywhere in the world. With our auto-generated client stubs for Java, Go, Node.js, Python and .NET, we can have you connected and coding in less than a minute!
Full cluster recovery from either planned shutdowns or cluster-wide crashes.
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).
Machine learning (ML) is being used almost everywhere, but the ubiquity has not been equated with simplicity. If you solely consider the operationalization aspect of ML, you know that deploying your models into production, especially in real-time environments, can be inefficient and time-consuming. Common approaches may not perform and scale to the levels needed. These challenges are especially true for businesses that have not properly planned out their data science initiatives.
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The Infinity Data research, commissioned in collaboration with Intel, examines how companies are addressing the challenge imposed by latency. The research was conducted through a survey of more than 350 IT decision-makers in the US and across industries: financial services, e-commerce, telecommunications, energy, and the public sector.
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.