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Building Real-Time Applications to Process Wikimedia Streams Using Kafka and Hazelcast
Learn how to build a real-time application to process Wikimedia streams using Kafka and Hazelcast.
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.
Combining the strengths and advantages of vector databases and real-time stream processing provides a unique developer experience and an efficient way of processing data in real-time at scale.
Hazelcast can process real-time and batch data in one platform, making it the right platform to use because it enriches your Kafka applications with “context.”
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.
Learn to update a Java client to automatically connect to a secondary, failover cluster if it cannot connect to its original, primary cluster.
Quickly learn how to connect Hazelcast Viridian to a Confluent Cloud Kafka cluster, and more!
Hazelcast Serverless manages your Cloud infrastructure, handling resource isolation, resource stealing, and isolated network access.
I’m happy to share Hazelcast will be kicking off 2023 by hosting #RTSPUnconf to connect with community members and industry experts on the Future of Real-Time Stream Processing.
Learn how to stream data from Apache Pulsar into Hazelcast, where you learn how to process data in real time.
Interesting read on @foojayio about the current state of the @grailsframework, at @TheASF
Written by @spoole167 interviewing @JamesFredley
https://foojay.io/today/grails-isnt-done-yet-part-1-inside-the-asf-reboot/
#Java 26 is here, and its main purpose is to provide a solid foundation for future things to come. It comes with new features, performance improvements and multiple enhancements–my blog post has all the info! 🚀
My #Java 26 article is now also available on Foojay Today! @foojayio
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