Deploying Snowplow on Kubernetes: Technical Q&A for Data Engineers

As the trend towards containerized, cloud-native infrastructure grows, many Snowplow users are exploring the deployment of their entire data pipeline on Kubernetes, leveraging platforms like AWS EKS and GKE. The Snowplow community confirms that running the full pipeline, including collectors, enrichers, loaders, and real-time processing infrastructure, is feasible on Kubernetes, despite some complexities and the need for custom engineering. Community resources like Helm charts and YAML files provide a starting point, though they often require customization, especially for handling IAM roles, logging, and metrics. Challenges such as IAM role binding issues, lack of Kafka support in some loaders, and the absence of unified Helm charts necessitate user intervention and adaptation. Best practices include defining the target stack, utilizing community charts, and following AWS IAM role practices, with ongoing community contributions enhancing the Kubernetes deployment experience for Snowplow users.