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 and GridGain provide performance and scalability for in-memory data grid use cases. Developers can embed instances in the application server or as process instances on dedicated hardware. Any member of a cluster can communicate with any other. Data is replicated across physical servers for redundancy. Both Hazelcast and Apache Ignite/GridGain support distributed and replicated Caches, Query and Execution.
But in almost all other in-memory computing and stream processing use cases, Hazelcast is the better choice. Hazelcast supports Reliable Topics, Ringbuffers, Maps, MultiMaps, Sets, Lists, HyperLogLogs, and EventJournal out of the box. Also, the Hazelcast CP subsystem is a component of a Hazelcast cluster that builds an in-memory strongly consistent layer. Its data structures always maintain linearizability and prefer consistency over availability during network partitions.
Hazelcast has run performance benchmarks against GridGain and makes the following assertions:
Our latest performance benchmark tests compare Hazelcast Enterprise 3.6 versus GridGain 7.4 (Apache Ignite 1.4.1). The table below shows some key performance metrics:
Gridgain (Apache Ignite)
Avg. operations/sec (higher is better)
Latency in ns (lower is better)
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.