This work is based on 17 months experience managing a digital agriculture platform that has aggregated and processed tens of gigabytes of data on 1500 cows on a commercial dairy farm. Significant challenges surfaced tied to multi-cluster management, fault-tolerance, and privacy as the number of applications and farm management models grew. To bridge this gap, we designed and implemented a cloud native networked system for multi-cluster configuration and management of farm data analytics that leverages KubeStellar and Software-Defined Farm paradigm. Our experience from designing, implementing and deploying this framework showcase how Kubernetes can enable farmers and agribusinesses to leverage the power of containerization and cloud-native computing to optimize workflows and streamline agricultural operations. This work presents progress towards cloud-native, scalable, and fault-tolerant data analytics in digital farming with potential environmental, financial, and societal impacts.