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Control-M 22: Fundamentals Automation API Developing (ASP)
By accessing the capabilities of Control-M via its Automation Application Programming Interface (API) from the Developers’ SelfService Portal, developers can work with workflow orchestration, further enhancing the self-service nature of the company’s DevOps processes.
This assisted self-paced training covers basic concepts of Control-M Automation API, how to write job definitions in JSON format, working with environments, validating and executing job definitions file, and how to review the job definitions in Control-M environment. In addition to this, it also talks about deploy and run services. Finally, students will also learn about advanced job definitions and the use of deploy descriptor.
Note: Web-Based Training (WBT) is available exclusively through our subscription offering and is not available for standalone purchase.
Access to instructor for up to 0.5 hours by appointment (use within 90 days of registration)
Virtual Lab
With this offering, you will receive student and lab guides as eBooks. You will have access to product and communities to answer your questions. All the course recordings will be available in the form of WBTs. You will have access to the instructors for up to 0.5 hours by appointment. Make sure to use this time up within 90 days of registration.
Lab Vouchers are issued at course registration time and must be redeemed within 90 days of receipt. Once Lab Voucher is redeemed, 28 days of lab access (portal access) are granted with 6 hours of on-demand lab time to be used. Once the lab time is used, or the 28 days expires, the lab access ends.
Click here for additional ASP virtual lab access information in a graphical format.
Course Lessons
Module 1: Getting Started With Control-M Automation API
Explain the purpose of, and differentiate between Control-M and Control-M SaaS
List the interfaces that can be used to access Control-M
Explain the purpose of Control-M Automation API
Explain the purpose of the Control-M Automation API Services
Access Control-M Automation API documentation
Install the Automation API command line interface
Generate an API Token
Access and use the Control-M Swagger UI
Use the Environment Service to connect an Automation API command line interface environment to the Control-M/EM
Use the Session Service to login to Control-M and create a session token
Module 2: Developing Jobs Using Automation API
Explain the process to build, deploy and run Jobs-asCode
Explain what Control-M Workbench is, and how to install it
Explain key Control-M concepts:
- Folders and jobs
- Workspaces
- New Day Process (NDP)
- Viewpoints
Define folders and jobs in the Planning domain of Control-M
Define folders and jobs in JSON code
Use the Build Service to validate JSON code
Module 3: Running Jobs
Use the Run Service to run folders and jobs in a Control-M environment
Use the Deploy Service to deploy folders and jobs to a Control-M environment
Module 4: Writing Jobs-as-Code
Define Defaults in code, to simplify Jobs-as-Code creation
Define dependencies between folders and jobs
Define folder and job schedules
Utilize the Control-M Python Client to define folders and jobs
Module 5: Using the Deploy Descriptor
Understand and use Automation API Deploy Descriptors
Assign, Replace and Add attributes when modifying code with a Deploy Descriptor
Use JSON Path to identify attributes
Filter which jobs/folders should be modified by using ApplyOn
Course Lessons
Module 1: Getting Started With Control-M Automation API
Explain the purpose of, and differentiate between Control-M and Control-M SaaS
List the interfaces that can be used to access Control-M
Explain the purpose of Control-M Automation API
Explain the purpose of the Control-M Automation API Services
Access Control-M Automation API documentation
Install the Automation API command line interface
Generate an API Token
Access and use the Control-M Swagger UI
Use the Environment Service to connect an Automation API command line interface environment to the Control-M/EM
Use the Session Service to login to Control-M and create a session token
Module 2: Developing Jobs Using Automation API
Explain the process to build, deploy and run Jobs-asCode
Explain what Control-M Workbench is, and how to install it
Explain key Control-M concepts:
- Folders and jobs
- Workspaces
- New Day Process (NDP)
- Viewpoints
Define folders and jobs in the Planning domain of Control-M
Define folders and jobs in JSON code
Use the Build Service to validate JSON code
Module 3: Running Jobs
Use the Run Service to run folders and jobs in a Control-M environment
Use the Deploy Service to deploy folders and jobs to a Control-M environment
Module 4: Writing Jobs-as-Code
Define Defaults in code, to simplify Jobs-as-Code creation
Define dependencies between folders and jobs
Define folder and job schedules
Utilize the Control-M Python Client to define folders and jobs
Module 5: Using the Deploy Descriptor
Understand and use Automation API Deploy Descriptors
Assign, Replace and Add attributes when modifying code with a Deploy Descriptor
Use JSON Path to identify attributes
Filter which jobs/folders should be modified by using ApplyOn