- Continuous Execution: Maintains persistent pipeline runs for real-time data processing scenarios
- Interval-Based Execution: Configures pipelines to run at specified intervals (hourly, daily, weekly, etc.)
- One-Time Execution: Schedules pipelines for single execution at a designated time
- Custom Timeline Configuration: Enables complex scheduling patterns with custom cron expressions
- Deadline Management: Sets completion time constraints to ensure pipelines finish within specified windows
- RESTful Integration: Exposes pipeline execution through standardized POST requests
- External System Integration: Allows third-party applications to trigger pipeline runs
- Programmatic Control: Enables dynamic pipeline orchestration through application logic
Step 3: After clicking “New Trigger,” you will be taken to a screen where you can create a trigger. Select the “Schedule” option and the UI pictured below ill populate.
Step 4: Complete the text fields below to configure your trigger based on the workflows requirements. You can also configure Run settings, SLA requirements, and Runtime variables in this UI.
Step 5: Click Create trigger to save your trigger.
Step 6: After saving your changes you will be taken to a new screen where you can enable your trigger. Once enabled, the trigger will begin to run based on your previous configuration.
Step 7: Click the “Save trigger in code” button to save your trigger to a YAML file.
Once your trigger is enabled, Frequency is the trigger plan: it controls the run schedule and actual run time. Schedule start only controls when that plan is allowed to begin. Choose Start at next Frequency time to let the trigger run at the next scheduled Frequency time, or choose Do not start before date/time to set the earliest allowed start. That date/time is not the run time; Frequency still controls when runs happen.
You have successfully automated your first Mage pipeline. This foundational automation capability enables consistent, reliable data processing without manual intervention. You can now apply these same principles to build additional automated workflows for your data infrastructure.