Request-response communication with REST / HTTP is simple, well understood, and supported by most technologies, products, and SaaS cloud services. Contrarily, data streaming with Apache Kafka is a fundamental change to process data continuously. HTTP and Kafka complement each other in various ways. This post explores the architectures and use cases to leverage request-response together with data streaming in the control plane for management or in the data plane for producing and consuming events.
How can I do request-response communication with Apache Kafka? That’s one of the most common questions I get regularly. This blog post explores when (not) to use this message exchange pattern, the differences between synchronous and asynchronous communication, the pros and cons compared to CQRS and event sourcing, and how to implement request-response within the data streaming infrastructure.
In the IoT world, MQTT and OPC UA have established themselves as open and platform-independent standards for data exchange in Industrial IoT and Industry 4.0 use cases. Data Streaming with Apache Kafka is the data hub for integrating and processing massive volumes of data at any scale in real-time. This blog post explores the relationship between Kafka and the IoT protocols, when to use which technology, and why sometimes HTTP/REST is the better choice. The end explores real-world case studies from Audi and BMW.
This blog post takes a look at cutting edge technologies like Apache Kafka, Kubernetes, Envoy, Linkerd and Istio to implement a cloud-native service mesh for a scalable, robust and observable microservice architecture.
This post shares a slide deck and video recording of the differences between an event-driven streaming platform like Apache Kafka and middleware like Message Queues (MQ), Extract-Transform-Load (ETL) and Enterprise Service Bus (ESB).
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