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- Traditional Scheduler – Using Eagle’s scheduler capabilities it is 's possible to build a workflow that triggers jobs in either a sequence , based on basic concurrency logic, or the state of a preceding event. This architecture typically uses a polling mechanism. In some cases the context of the workflow is not maintained, that is, load securities poll for changes based on the update date and execute an engine on securities that have changed and then publish.
- Data Driven – In a data driven workflow, the context is typically maintained and managed through a series of control messages to maintain and interrogate the status of the service. Using the preceding example, load securities , and then pass changed securities to the engine for processing. There is no need for a polling process, the consumer is fed the exact payload to process.
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A customer is using Control-M to orchestrate key jobs within their enterprise. The user configures the necessary file moment jobs in the job scheduler and then wishes to invoke Eagle’s Start of Day process. This process involves a number of tasks that need to be completed in a defined order. In this scenario, the team has created a “coarse coarse grained service” service called SOD. The workflow for this service is handled within Eagle, but at the completion of this service the orchestration and management passes back to the enterprise scheduler. In this scenario both the Eagle orchestration and external orchestration patterns have been used.
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If you are using the XSCHEDULE toolkit today, the external orchestration provides a number of benefits such as alternative transport models, decoupled integration (For example, cloud to cloud) and enhanced management capabilities with regard to the control and reporting of jobs.
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