The popularity of AI features like machine learning and data services has created a need to collect data from multiple sources, like customer-facing websites, edge devices, databases, etc. This has increased the demand for high-performance stream processing, also known as "data in motion." Data in motion refers to digital information flowing in and out of the system components. It is essential for organizations that need to support a continuous, real-time flow of data to deliver rich, in-the-moment customer experiences and drive efficiency within business operations.In this talk, we will discuss different techniques for collecting data and mainly focus on how Quarkus, a Java Cloud-Native stack, lets you build Kafka-based event-driven architectures. Using a sample application, we will illustrate the concepts and common patterns and show how Quarkus eases the development and deployment of data-in-motion architectures on OpenShift.