Multi-Cloud Streaming Data Integration with Confluent and Krake

This post explores the integration of Confluent Platform with Krake, an open-source workload scheduler, to enable flexible workload placement and data transparency across distributed and heterogeneous datacenters. It highlights how this combination addresses legal, technical, and energy limitations of traditional centralized cloud computing by allowing applications to be run on datacenters that fit their specific requirements. The post details how Confluent Platform's global capabilities (Multi-Region Clusters, Cluster Linking) create a globally distributed event streaming fabric, while Krake orchestrates containerized and virtualized workloads based on user requirements and platform characteristics, optimizing for energy efficiency and cost. It introduces two workload types: 'customer-bound' (stateless services) and 'resource-bound' (batch processing like ML training), and discusses a third type, 'distributed streaming application,' which leverages variable cloud computing costs and energy prices for flexible workload placement across regions.