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!
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industry’s leading in-memory computing platform.
The in-memory speed you count on, with the convenience and scalability of cloud.
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.