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!
These demands often drive the purchase of expensive and proprietary solutions such as Pivotal Gemfire. Although Gemfire is a mature product with deployments across industries such as financial services, travel, gaming, and others, several critical issues are driving projects to Hazelast IMDG for many customers worldwide. These issues include:
Licensing model: Gemfire’s licensing model is a traditional closed-source proprietary license, which restricts how the product can be used and deployed. Hazelcast IMDG is offered under the Apache 2 open source license (a commercially licensed version with additional capabilities is also available). Open source licensing provides more freedom and flexibility to get started quickly and validate your use cases.
Aging technology: Gemfire is feature rich, but the origins of Gemfire come from Gemstone, an object database company for Smalltalk that was founded in 1982. It was built on much earlier versions of Java, and the production status of Gemfire deployments prohibits rearchitecting the product. This has led to a radical slowdown in product innovation and a tendency to create complex workarounds for advanced use cases.
Lack of support: The brain-drain associated with new acquisitions and the complexity of the Pivotal organizational structure makes it harder for the company to support Gemfire customers. Their ability to support customers post-sale through deployment and beyond is diminishing. In contrast, Hazelcast® is concentrating its hiring and technical talent around a sole focus – to be the #1 in-memory computing technology.
Complex configuration: Gemfire is complex to configure and get running. In contrast, Hazelcast is easy to use and enables your technology team to get hands-on with Hazelcast IMDG within minutes and to deploy in the time that Gemfire is provisioning evaluation licenses or negotiating a paid proof-of-concept or pilot.
Increasingly, organizations are turning to Hazelcast IMDG in preference to Pivotal Gemfire. Many top banks, ecommerce companies, and telecommunications vendors have done so. Most of these companies report the ownership and cost issues listed above eventually became too much for them.
Hazelcast IMDG has been reported not only to be more cost-effective and to provide a better ownership experience through open source, but it is also considered radically easier to deploy and more performant.
This whitepaper provides a point by point comparison of Hazelcast IMDG and Pivotal GemFire.
This white paper provides a point by point comparison of Hazelcast IMDG® and Pivotal GemFire.
In-Memory data grids have historically been the exclusive domain of large investment banks and proprietary solutions such as Oracle Coherence, Pivotal Gemfire and Software AG Terracotta. Hazelcast provides an opensource solution that is easy to develop, elastic in scaling and fault tolerant.
First part of presentation will cover simple use case, fictional stock brokerage system, that shows basic distributed structures and their behavior.
Second part will show some advanced features of Hazelcast like event listeners and data affinity.
At the end comparison between Hazelcast, on one side, and redis and memcached, on the other is going to be presented.
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.