Learn how to stream data from Apache Pulsar into Hazelcast, where you learn how to process data in real time.
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Hazelcast + Kibana: Best Buddies for Exploring & Visualizing Data
In this article I describe how you can benefit from such a data visualization front-end without writing a single line of code.
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Hazelcast, from Embedded to Client-Server
Java developers are particularly spoiled when using Hazelcast. Because Hazelcast is developed in Java, it’s available as a JAR, and we can integrate it as a library in our application.
Just add it to the application’s classpath, start a node, and we’re good to go. However, I believe that once you start relying on Hazelcast as a critical infrastructure component, embedding limits your options. In this post, I’d like to dive a bit deeper into the subject.
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Billion Events Per Second with Millisecond Latency: Streaming Analytics at Giga-Scale
We’re preparing a scientific paper on Hazelcast Jet, describing its architecture based on symmetric, data-local, non-blocking distributed event processing. As a part of this effort, we implemented the vendor-neutral NEXMark benchmark suite, consisting of 8 streaming queries that aim to capture typical kinds of questions you’re likely to ask about your real-time data.
The queries deal with a domain model of auctions, sellers, and bids. For example, Query 5 asks: “Which auctions have achieved the highest price in the last period?”
