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.
Ensar’s primary areas of interest are distributed data, replication, consistency, and storage. He has more than seven years of hands-on expertise in designing, developing, and testing distributed algorithms, with solid experience in concurrency. He has authored a number of articles on distributed data and stream processing, and is a frequent speaker at industry conferences on topics such as replication and distributed systems. Several of his talks can be found on YouTube, including Replication Distilled, Distributed Systems for Mere Mortals, and Replication in the Wild. Ensar is a Ph.D. candidate in computer science at Bilkent University in Ankara, Turkey.
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At Hazelcast we take reliability very seriously. With the new CP Subsystem module, Hazelcast has become the first and only IMDG that offers a linearizable distributed implementation of the Java concurrency primitives backed by the Raft consensus algorithm. In addition to well-grounded designs and proven algorithms, reliability also requires a substantial amount of testing. We […]
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Stream processing is a paradigm for on-the-fly processing of unbounded data feeds. We have been witnessing that stream processing engines (SPEs) get more attention every day in the era of fast data and become a fundamental component of data processing pipelines. They usually run in distributed settings to be able to cope with the flood […]
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