The Control-M for Kubernetes plug-in enables you to do the following:
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Run one or more pods to completion in a Kubernetes cluster
This enables you to integrate Control-M capabilities, such as advanced scheduling criteria, status monitoring, and SLA management.
Kubernetes job specs for this purpose can be retrieved from a remote location during job execution using a REST request or can be uploaded during job definition as local YAML files. In addition, you can provide job specs as templates with placeholders for job spec parameters. -
Run OS jobs on remote UNIX-based hosts outside the Kubernetes cluster in an agentless manner
To run these OS jobs, the Agents in Kubernetes connect to multiple hosts outside the cluster using the SSH protocol. -
Use Control-M Managed File Transfer (MFT) to:
- Pull files from central storage (such as S3) into a persistent volume in the Kubernetes cluster and process them by running application pods.
- Transfer files generated in the persistent volume during application processing to central storage outside the cluster.
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Use containerized Agents running in Kubernetes to consume remote services
This enables you to run the Agents on an optimized and highly scalable platform while executing a variety of workloads in a secure and fully automated manner.
The main branch contains general information, overview material, and shared resources related to the Control-M for Kubernetes plug-in.
Each example is provided as a dedicated folder in the repository root.
Every example focuses on a specific capability or integration pattern and includes step-by-step guidance and sample configurations.
- 01-Remote_Job_Specification_sample
Demonstrates how to deploy a containerized Remote Specification service that dynamically generates Kubernetes Job specifications for Control-M jobs at runtime.
Additional examples will be added over time.
Found a bug? Looking for a new feature? Check if we already have an open issue in the GitHub Issues page.
If not, feel free to create a new one. Together with the community, we will review, prioritize, and address relevant feedback and enhancement requests.