As organizations increasingly migrate data and processing workloads to cloud environments, hybrid cloud solutions and cloud-native application architectures are gaining traction, particularly in the context of machine learning and artificial intelligence. Amid this transition, energy efficiency becomes crucial, prompting the exploration of decentralized cloud systems composed of multiple geographically distributed datacenters. The Confluent Platform, based on Apache Kafka, and the open-source scheduler Krake from Cloud&Heat, are pivotal in this shift by facilitating flexible workload migration and real-time data streaming. This approach allows applications to be deployed based on ecological and economic factors, optimizing energy usage and enhancing data sovereignty. By creating a globally distributed data plane, Confluent ensures transparent access to event streams across datacenters, while Krake optimizes workload placement based on latency, cost, and energy efficiency. The synergy of these technologies enables scalable, reliable, and energy-efficient cloud-native applications, paving the way for advanced data processing and event-driven architectures.