Stream processing: Continuous data management in real time

Stream processing lets developers view, analyze, and combine data from a wide variety of sources.

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Featured image: event-driven distributed serverless workflow
Jul 02, 2024

Build an extendable multichannel messaging platform

Bruno Meseguer

This solution demonstrates an implementation to build a platform that...

Building resilient event-driven architectures with Apache Kafka
Jun 26, 2024

Open innovation: Red Hat’s impact on the Kafka and Strimzi ecosystem

Simon Woodman

Open source is at the heart of everything Red Hat works on. Learn how Red Hat...

Building resilient event-driven architectures with Apache Kafka
Jun 20, 2024

Stateful and reactive stream processing applications with Apache Kafka, Quarkus,...

Hans-Peter Grahsl

Learn how to build an end-to-end reactive stream processing application using...

Building resilient event-driven architectures with Apache Kafka
Jun 13, 2024

How to manage and preserve Kafka Connect offsets smoothly

Abdellatif Bouchama

Get tips on managing and preserving Kafka Connect offsets smoothly starting...

Stream processing resources

Building resilient event-driven architectures with Apache Kafka
Article

Encryption at rest for Apache Kafka

Tom Bentley +1

Discover the importance of encryption of data at rest in Apache Kafka and examine one approach to encrypting data at rest using the Kroxylicious Kafka proxy.

Building resilient event-driven architectures with Apache Kafka
Article

Kafka tiered storage deep dive

Federico Valeri +1

Explore Apache Kafka's tiered storage feature, which helps scale compute and storage resources independently and provides better client isolation.

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