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How to Set Up Your Own On-Premises Hazelcast on Kubernetes

Rafal Leszko

Hazelcast loves Kubernetes. Thanks to the dedicated Hazelcast Kubernetes plugin, you can use dynamic auto-discovery. Hazelcast on Kubernetes can also run in multiple topologies: embedded, client-server, or as a sidecar. What’s more, thanks to the Helm package manager and the dedicated Hazelcast Helm Chart, you can deploy a fully functional Hazelcast server in literary minutes. […]

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Bitcoin’s Death Cross

Neil Stevenson

Let’s start off the New Year with a fun code example! This example shows how Jet is used to spot the dramatically-named Death Cross for the price of Bitcoin, which is an indication to sell, Sell, SELL!. The idea here is that we could automatically analyze stock market prices and use this information to guide […]

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Hazelcast with Istio Service Mesh

Mesut Celik

Istio is said to be the next thing if you follow the Kubernetes path, and service meshes are mentioned whenever you go to a cloud-native meetup or conference like KubeCon. However, you do not always leverage something for the sake of popularity. The Hazelcast-Istio support discussion was started by a user after a GitHub issue […]

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Hazelcast Resilient to Kubernetes Zone Failures

Rafal Leszko

Data is valuable. Or I should write, some data is valuable. You may think that if the data is important to you, then you must store it in the persistent volume, like a database or filesystem. This sentence is obviously true. However, there are many use cases in which you don’t want to sacrifice the […]

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Stream Deduplication with Hazelcast Jet

Jaromir Hamala

Hazelcast Jet 3.2 introduces stateful map, filter, and flatmap operations, which are very strong primitives. In this blog, I am going to show you how to use stateful filter for detecting and removing duplicate elements in a stream. Why Deduplication? Deduplication is often used to achieve idempotency or effectively-once delivery semantics in messaging systems. Imagine […]

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How to Set Up Your Own On-Premises Hazelcast on Kubernetes

Rafal Leszko
by Rafal Leszko | January 27, 2020

Hazelcast loves Kubernetes. Thanks to the dedicated Hazelcast Kubernetes plugin, you can use dynamic auto-discovery. Hazelcast on Kubernetes can also run in multiple topologies: embedded, client-server, or as a sidecar. What’s more, thanks to the Helm package manager and the dedicated Hazelcast Helm Chart, you can deploy a fully functional Hazelcast server in literary minutes. […]

Bitcoin’s Death Cross

Neil Stevenson
by Neil Stevenson | January 06, 2020

Let’s start off the New Year with a fun code example! This example shows how Jet is used to spot the dramatically-named Death Cross for the price of Bitcoin, which is an indication to sell, Sell, SELL!. The idea here is that we could automatically analyze stock market prices and use this information to guide […]

Hazelcast with Istio Service Mesh

Mesut Celik
by Mesut Celik | December 09, 2019

Istio is said to be the next thing if you follow the Kubernetes path, and service meshes are mentioned whenever you go to a cloud-native meetup or conference like KubeCon. However, you do not always leverage something for the sake of popularity. The Hazelcast-Istio support discussion was started by a user after a GitHub issue […]

Hazelcast Resilient to Kubernetes Zone Failures

Rafal Leszko
by Rafal Leszko | December 03, 2019

Data is valuable. Or I should write, some data is valuable. You may think that if the data is important to you, then you must store it in the persistent volume, like a database or filesystem. This sentence is obviously true. However, there are many use cases in which you don’t want to sacrifice the […]

Stream Deduplication with Hazelcast Jet

Jaromir Hamala
by Jaromir Hamala | November 12, 2019

Hazelcast Jet 3.2 introduces stateful map, filter, and flatmap operations, which are very strong primitives. In this blog, I am going to show you how to use stateful filter for detecting and removing duplicate elements in a stream. Why Deduplication? Deduplication is often used to achieve idempotency or effectively-once delivery semantics in messaging systems. Imagine […]

Hazelcast Delivers Ultra-Fast Cloud Application Performance in IBM Cloud Pak for Applications

Kelly Herrell
by Kelly Herrell | November 05, 2019

We are excited to announce that Hazelcast in-memory technologies are now available for sale from IBM with the IBM Cloud Pak™ for Applications. The Hazelcast in-memory computing platform is an ultra-fast processing architecture for mission-critical applications where microseconds matter. The world’s most data-centric companies complement their systems of record, such as databases, with in-memory solutions […]

Hazelcast IMDG 4.0 BETA is Released

Matko Medenjak
by Matko Medenjak | November 05, 2019

It is my pleasure to announce that after 6 years a new major version of Hazelcast has been released! This new release brings a breath of fresh air into Hazelcast while also being more robust than ever. We did try to keep enough familiarity to not surprise our users too much, but we also invested […]

In-Memory Computing for All (Ft. Intel Optane)

Dale Kim
by Dale Kim | November 05, 2019

If you had a choice of processing data in-memory versus not in-memory, all other things being equal, wouldn’t you always choose in-memory? You might not need the higher performance, but if it were available to you, you’d take it because faster is always better than slower, right? It would be wonderful if we lived in […]

Hazelcast Autoscaling with Horizontal Pod Autoscaler (HPA)

Mesut Celik
by Mesut Celik | October 30, 2019

Cloud technologies give you on-demand options so that you can create compute, disk, or network resources based on your requirements. When your demand changes, you update the infrastructure by releasing some resources or adding more. That is actually named “manual scaling” which is based on human intervention. Kubernetes is no different in this particular use […]

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