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Control-M orchestrates application workflows to ensure critical business services operate efficiently and according to expected service levels. When errors do occur, problems can be analyzed and corrected quickly, with easy access to failure information such as logs and a consistent set of tools for manipulating workflow components. Taking a Jobs-as-Code approach enables developers and engineers to build workflows and other artifacts using JSON and to interact with Control-M via Automation API which provides RESTful web services and a node.js command line interface. The examples in this section are fully functional tutorials that describe Control-M Automation API use cases implemented by customers . Tutorials are placed into one of the following categories:

  1. Dynamic Infrastructure - managing Control-M workload in virtualized, cloud and containerized environments

To meet the ever-increasing demands for computing power at the click of a button while paying only for the resources required right now, a broad range of approaches have evolved for satisfying this need including:

    • Cloud solutions like AWS, Azure, Google Cloud, IBM Cloud, Oracle Cloud and OpenStack
    • Containerization solutions like Docker, Linux Containers and CoreOS Rocket
    • Cluster managers like Kubernetes and Docker Swarm
    • Ephemeral infrastructure with auto-scaling and transient machines like EC2 instances
    • Immutable servers
    • Virtualization with VMware and similar solutions

Since modern business applications run on all the above as well as traditional infrastructure, Control-M provides orchestration across all of this diversity with a combination of REST APIs and traditional interfaces. These tutorials describe sample implementations for orchestrating business applications in all of these technology stacks whether used individually or in any combination.

  1. Jobs-as-Code supporting the integration of Control-M into a CI/CD automated delivery pipeline
  2. Operational Efficiency - optimizing traditional usage of and access to Control-M capabilities through Automation API
  3. Big Data - Orchestrating Data Pipelines, Machine Learning and Analytics using Hadoop ecosystem components

Additional tutorials will be published as they are developed, so visit this site from time to time to find new material. If there is a tutorial you would like to see, please submit your ideas and comments here.

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