These guides demonstrate the operational flexibility and speed of the Hazelcast In-Memory Computing Platform. Set-up in seconds, data in microseconds. Operations and developer friendly.
Find out for yourself how to get a Hazelcast IMDG cluster up and running. In this Getting Started guide you’ll learn how to:
Learn how to run a distributed data stream processing pipeline in Java. In this Getting Started guide you’ll learn how to:
As a next step, you will be able explore the code samples and extend your setup more features and connectors.
In-depth technical discussions about in-memory systems and solutions.
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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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
We are excited to introduce Hazelcast Jet 3.2, the latest version of Hazelcast’s batch and stream processing platform. We’ve added a number of powerful capabilities that not only expands the addressable use cases, but also further simplifies your application development efforts. Stateful Mapping Operator Jet now has support for more general fault-tolerant, stateful operations that […]
