Jobs-as-Code is a DevOps approach to making workflows versionable, testable, and maintainable—helping developers, engineers, and SREs collaboratively define, schedule, manage, and monitor application workflows in production.
Code jobs using any text editor or IDE all in an automated CI/CD pipeline
Use your existing build tools to build the automation
Perform automated testing using existing testing framework
Deploy to downstream environments
Perform Operational Tasks
Check status of agents in hostgroups
Gather hostgroups, find the agents, and check their status automatically.
Define a remote host to run jobs without deploying any scheduling agent.
Manipulate workload policies
Self-service for flexing workload volume during high-activity or system maintenance.
Modify user roles
Update users or roles based on changes in their responsibilities or HR status.
Get reports via automation API
Verify status of agents within hostgroups.
Order jobs from automation tools
Submit or manipulate Control-M jobs from other automation tools.
Use CI/CD Solutions to Manage Control-M Jobs
Get started with Jobs-as-Code in delivery pipeline
Use a Jobs-as-Code approach to embed Control-M artifacts in a CI/CD pipeline.
Example of Gitlab CI/CD pipeline for Control-M artifacts
Operational example of a Gitlab pipeline that includes Control-M jobs and deploy descriptors.
Folder cleanup utilities
Python utility for detecting and cleaning up folders in Control-M when a deployment removes folders.
CWCM: Integrate Control-M into DevOps CI/CD lifecycle (Webinar)
Use common tools such as Jenkins and git to integrate Control-M into your software development lifecycle.
Install/Configure Control-M Components in Clouds & Containers
Control-M services from Lambda
Invoke Control-M services from Lambda function triggered by an AWS event.
Control-M Server in Kubernetes pod
Run Control-M/Server as a Kubernetes service.
Control-M Agent in Kubernetes pod
Run Control-M/Agent with horizontal pod autoscaling.
Use case: Control-M/Agent as Kubernetes DaemonSet
Complete example including Python client using Kubernetes API for submitting jobs and monitoring.
Maintain and upgrade Control-M
Install fix packs, upgrade, and install optional components.
Use Terraform to provision Control-M
Install a Control-M environment on AWS with Terraform.
Integrate IDEs with Control-M Services
IDE and code editor integration
Invoke Control-M functions and access code snippets within various IDEs and editors.
Connect API Gateways & Control-M Automation API
Access Control-M REST API services via API gateway
Considerations for accessing Control-M RESTful web services via an API gateway.
Apigee API gateway quick setup
Set up Apigee gateway to use with Control-M automation API.
See what customers love about Control-M
Control-M jobs produce the reports so we don't have to create them every day
Our developers leverage the “as-code” interfaces and it makes it very easy to roll out new applications and application updates 詳細
Rolling out new applications and application updates is much faster
Earlier, we had to go through a lot of processes …Now, they are directly creating those jobs and submitting them. It is coming in automatically because it is running in Control-M." 詳細