Kafka Processing Error Handling in Kafka Streams Introduction A major concern when developing Kafka Streams applications is handling processing errors. Processing errors occur when the implemented logic in a Kafka Streams application fails to process a record correctly. Let’s illustrate what processing errors are with a concrete example from our Supply Chain domain: a DeliveryBooked event
EventDriven Designing Kafka Streams Applications For the past 4 years, our journey into the heart of Kafka's capabilities has been shaped by two pivotal concepts: Master Topologies and Micro Topologies. These conceptual frameworks have become the backbone of our Kafka Streams application design, offering a comprehensive and granular understanding of our end-to-end communication.
How to 'Kstreamplify' : your new way to develop Kafka Streams application Spoiler alert * After Ns4Kafka feature to bring controllers on top of Kafka resources (Topics, Kafka Connect and Schema Registry) * After kafkactl CLI linked with Ns4Kafka that lets you deploy your Kafka resources using YAML descriptors. We're thrilled to announce a brand new open source tool around Kafka: a
Architecture Migrate Applications from Kafka On-Premise to Confluent Cloud Michelin introduced Kafka back in 2018 with some on-premises clusters. In my previous article, I shared how Kafka helped Michelin to learn distributed architectures as well as the complexities and difficulties we faced when managing these Kafka clusters. In this article I would like to explain how we migrated all
Architecture “The metamorphose” of our Information System by Implementing a distributed event streaming platform In this article, I want to share Michelin's experience on its first implementations of Kafka and distributed architectures, which is part of the early stages of implementing our EDA architectures. Our firsts experiences we had on a distributed platform.
