An extensible framework for application-level data management and protection workflows on Kubernetes.
Kanister is an extensible framework for application-level data management on Kubernetes. It provides APIs for defining and curating data protection workflows, such as backup, restore, and data migration, by abstracting the complexities of executing these operations in Kubernetes environments. It solves the problem of managing data operations in a cloud-native, declarative manner.
Kubernetes administrators, DevOps engineers, and platform teams who need to implement and manage data protection workflows for stateful applications running on Kubernetes clusters.
Developers choose Kanister for its Kubernetes-native design using CRDs, storage-agnostic flexibility, and extensible blueprint system that simplifies complex data operations. Its integration with Kubernetes RBAC and observability tools provides a secure and manageable solution for data protection workflows.
An extensible framework for application-level data management on Kubernetes, Kanister is a Cloud Native Computing Foundation sandbox project and was originally created by the Veeam Kasten team.
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Implements Custom Resource Definitions (CRDs) that seamlessly integrate with Kubernetes' declarative management and security models, as highlighted in the README's emphasis on conforming to Kubernetes' distribution models.
Allows efficient data transfer to any object storage service using its APIs, preventing vendor lock-in and offering flexibility, as stated in the README's storage-agnostic claim.
Provides atomic data operation functions and reusable blueprints for detailed backup and restore steps, enabling customization for applications like PostgreSQL or MongoDB, as shown in the sample blueprints repository.
Leverages Kubernetes RBAC for access control and exports logs, events, and metrics to tools like Prometheus and Grafana, ensuring secure and monitorable workflows per the README's features.
Requires developers to write and maintain custom blueprints for each application, which can be time-consuming and error-prone, especially for unique workloads not covered by sample blueprints.
While sample blueprints exist for common databases, support for less popular or proprietary applications may require significant custom development, relying on community contributions as indicated in the blueprints repository.
Executing data operations through Kubernetes job pods can introduce latency compared to native storage integrations, potentially impacting backup and restore times for large datasets, a trade-off of the abstracted architecture.