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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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.
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This global pizza delivery chain operates in 82 countries, making it the second-largest franchised pizza chain in the world. Today, it operates more than 12,600 pizza restaurants around the world and delivers more than 1 million pizzas each day.
Gamesys, the award-winning online gaming company, required a highly scalable poker platform that could be used for both real money and social gaming. The challenge was to build a cost-effective platform that could manage failures without losing a player’s money, was highly available and elastic and could handle tens of thousands of concurrent players.
Stream processing is the new critical application for today’s always-on, always available digital ecosystem. While stream processing can cover a wide range of applications (healthcare, IoT, payment processing, etc.), Hazelcast’s stream processing engine - Jet - has had an interesting and unusual deployment in the oil and gas drilling domain, with spectacular results.
A top ten US bank was hitting transaction rate limits when trying to apply fraud detection algorithms against customer data sitting on its old relational database platform. This technically imposed restriction was causing them to break fraud detection SLAs and to ultimately become a blocker on new business.
In this guide, you will learn how to use Hazelcast distributed caching with Spring Boot and deploy to a local Kubernetes cluster.
In this guide, you will learn how to use Hazelcast distributed caching with MicroProfile and deploy to a local Kubernetes cluster. You will then create a Kubernetes Service which load balances between containers and verify that you can share data between microservices. The microservice you will deploy is called hazelcast-microprofile. The hazelcast-microprofile microservice simply helps […]
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.
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