To set a schedule between intervals in Azure Data Factory, you can use the following steps:
Navigate to the Azure Data Factory where your pipeline is located and select the pipeline that you want to schedule.
Click on the "Publish All" button in the top-right corner of the page to publish your changes.
Click on the "New/Edit" button under the "Triggers" section to create a new trigger or edit an existing one.
In the "New/Edit Trigger" window, select the "New/Edit" button under the "Integration runtime" section to specify the schedule.
In the "Integration runtime" section, you can set the schedule by specifying the start time, the end time, and the frequency of the schedule. You can set the frequency to "Daily", "Weekly", or "Monthly" and specify the days and times that you want the pipeline to run.
Once you have specified the schedule, click the "OK" button to save the changes.
Click the "Publish All" button again to publish the changes to the Azure Data Factory.
By following these steps, you can set a schedule between intervals in Azure Data Factory and control when your pipeline runs. Note that the schedule may be affected by the time zone of the Azure Data Factory, so make sure to adjust the schedule accordingly.
To validate the schedule, you can check the "Monitor and Manage" section in the Azure Data Factory. This section displays the status of all the pipelines and their triggers. You can see the next run time and the history of previous runs. If the schedule is not running as expected, you can troubleshoot by checking the logs and error messages in this section.
You can also modify the schedule at any time by repeating the above steps and updating the schedule as needed.
Finally, it's a good practice to regularly review and update the schedule to ensure that your pipelines are running optimally and that the data is being processed in a timely manner.
By following these steps and regularly reviewing and updating the schedule, you can ensure that your pipelines are running as expected and that your data is being processed efficiently in Azure Data Factory.