In this video, discover how Broadcom's ESP dSeries Workload Automation simplifies cloud orchestration by seamlessly automating, scheduling, and managing complex workloads across hybrid and cloud environments. Learn how this powerful and flexible solution coordinates on-premises and cloud-hosted activities from a single, centralized point of control to help organizations optimize resource allocation, maximize operational efficiency, and reduce manual intervention across the entire modern enterprise.
Video Transcript
| Note: Transcript created with the assistance of generative AI |
Moving from a traditional on-premises scheduling environment to orchestrating a mix of cloud services can feel intimidating. Disconnected scripts and cross-platform monitoring often lead to a chaotic guessing game. But the enterprise batch scheduling skills you already possess translate directly to the cloud. By using dSeries as your central command hub, the logic of automation remains the same.
The Four Key Concepts
This text block outlines the four key concepts that form every cloud workflow:
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The Application: Acts as your container.
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The Job: Represents the single task.
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The Dependency: Dictates the execution order.
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The Event: The specific trigger that sets the application into motion.
The Seven-Step Construction Process
Steps 1–3: Defining the Application and Jobs
These four concepts, paired with authenticated credentials on your agent machine, provide the foundation to begin the seven-step construction process.
The construction process begins in the Define perspective. Creating a new application provides a blank canvas for your pipeline, allowing you to group related cloud tasks under a single business goal. Looking closer at step three, you drag and drop new jobs into the workspace. For cloud tasks, you rely on two main types:
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REST API jobs to communicate with cloud services.
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Command jobs to execute local CLIs on the agent.
Steps 4–5: Dependencies and Error Handling
Step four details how you establish execution order. By drawing lines between your jobs, you force dSeries to wait. A REST API job tasked with processing data will not execute until the preceding command job confirms the file transfer is successful.
Step five addresses the reality of network interruptions and API timeouts. In the notifications tab, you configure failure alerts for your operations team and instruct dSeries to automatically retry a failed cloud job after a designated delay. Requiring a success code from the cloud API before releasing the next task stops cascading pipeline errors. This logical structure ensures that dSeries only moves forward once it has mathematical confirmation that the previous resource is finished.
Steps 6–7: External Events and Execution
Consider a scenario where the application must remain idle until a specific payload of data arrives on a local server. This requires a trigger that looks beyond a simple time schedule to start the pipeline. As step six shows, you define an event based on an external condition. You can instruct dSeries to monitor an FTP server and trigger the application the moment the required file arrives.
Step seven covers the final execution. You simulate your event logic to ensure your dependencies are sound. Once triggered, you switch to the monitor perspective to watch the jobs execute in real time, checking the log files immediately if any cloud API returns an error. Following these seven distinct steps transforms isolated cloud commands into a resilient, tracked enterprise workflow.
Multi-Cloud Integration Scenarios
The true strength of this structural framework is that it operates agnostically. The architectural rules remain constant regardless of the specific cloud provider you use.
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Amazon Web Services (AWS): This guide details practical scenarios for Amazon Web Services. You can execute AWS CLI commands to automatically shut down non-production EC2 instances on weekends, or you can orchestrate Amazon EMR data pipelines using dependencies to guarantee the cluster is terminated the moment the processing script finishes.
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Google Cloud Platform (GCP): The same logic applies to Google Cloud Platform. You can configure a dSeries job to wait for an on-premises database backup to finish and then execute a command job to trigger SQL aggregations in BigQuery or sync local logs into Google Cloud Storage.
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Microsoft Azure: These Azure integrations allow you to synchronize legacy mainframe events with modern cloud infrastructure. You use REST APIs to trigger Azure Data Factory pipelines the moment a local extraction finishes or offload heavy compute tasks seamlessly to Azure Batch.
Conclusion
AWS EC2 instances, GCP BigQuery Analytics, or Azure Data Factory pipelines all follow the same seven-step pattern: define the application, place the jobs, draw the dependencies, and let dSeries manage the cloud complexity.
