Learn how to build a real-time application to process Wikimedia streams using Kafka and Hazelcast.
When Speed Matters: Real-time Stream Processing with Hazelcast and Redpanda
Let’s explore the powerful combination of Hazelcast and Redpanda to build high-performance, scalable, and fault-tolerant applications that react to real-time data.
How to Build and Deploy a Real-time Cloud-based Logging System
Learn how to treat logs and traces as part of a scalable cloud storage repository that can be analysed with the techniques used for big data.
How to Create a Failover Client using the Hazelcast Viridian Serverless
Learn to update a Java client to automatically connect to a secondary, failover cluster if it cannot connect to its original, primary cluster.
Streaming Real-Time Data on the Hazelcast Viridian Serverless
Quickly learn how to connect Hazelcast Viridian to a Confluent Cloud Kafka cluster, and more!
How to Get Started with the Hazelcast Viridian Serverless
Hazelcast Serverless manages your Cloud infrastructure, handling resource isolation, resource stealing, and isolated network access.
Real-time Stream Processing with Hazelcast and StreamNative
Learn how to stream data from Apache Pulsar into Hazelcast, where you learn how to process data in real time.
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.
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.
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?”