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!
Get a 30-day free trial.
Get started today with the
industry’s leading in-memory computing platform.
The in-memory speed you count on, with the convenience and scalability of cloud.
Event stream processing continues to play an increasingly important role in today’s data architectures. This is no surprise, considering that companies are striving to respond faster to ongoing changes in their business environments. However, these companies are still not taking full advantage of the value of their data, typically because they have not planned for the right approaches and architectures for stream processing. Read this Gartner report to learn more.
Hazelcast Cloud delivers enterprise-grade Hazelcast software in the cloud, deployed as a fully managed service. Leveraging over a decade of experience and best practices, Hazelcast Cloud delivers a high-throughput, low-latency service that scales to your needs while remaining simple to deploy. If you’re considering moving to the Cloud, or are looking for an easy ramp on deploying in-memory technology, this white paper on migrating in-memory to the cloud is an informative and helpful resource.
Edge computing complements your cloud deployments by addressing issues related to having data created in remote locations. While businesses today are still in the early stages of edge computing, the expectation is that there will be significant adoption in the next two years. Hazelcast believes now is a good time to explore edge opportunities, and supports such initiatives with in-memory technologies that help drive powerful edge deployments.
No posts were found matching that criteria.
Read about how we compare to Oracle Coherence. While Hazelcast cannot publish our performance benchmark results against Coherence (due to restrictions in the Oracle Technology Network License), we have a benchmark suite we can share with you for your own testing.
Hazelcast has run performance benchmarks against GridGain that show Hazelcast has up to 90% more throughput with lower latencies in fair, comparably configured setups.
Hazelcast has performed a number of performance benchmarks against Redis over multiple software versions, showing that in fair, comparably configured setups, Hazelcast outperforms Redis, especially at scale.
In this White Paper, Hazelcast provides business-level examples of how 5G and In-Memory can work together for Edge Use cases.
When microseconds can mean the difference between success and failure, caching solutions deliver blinding speed with scalable and flexible management of data. In this white paper, we cover specific of caching solutions, where and why an enterprise would be interested, its application across multiple industries and its impact on data management, including Artificial Intelligence and Machine Learning use cases.
This white paper provides a point by point comparison of Hazelcast IMDG and Redis across a number of dimensions.
This guide steps through the process of upgrading your instance of Hazelcast IMDG.
The streaming benchmark is intended to measure the latency overhead for a streaming system under different conditions such as message rate and window size. It compares Hazelcast Jet, Apache Flink, and Apache Spark Streaming.
There are no more posts.