As needed, you can run a schedule at any time in between scheduled executions. The processes of the schedule are queued in the platform and are executed as soon as resources become available.
You can also run a schedule on-demand via the API for executing schedules.
If you run a process at an ad-hoc interval, duplicate data may get loaded.
Depending on the volume and complexity of the process, executing a process during peak hours can impact performance of the GoodData project that it is updating. Where possible, execute processes during off-peak hours.
Create one or more data validation reports to identify how your processes are working. To see the effects of your processes in your GoodData projects, open a different browser tab and navigate to https://secure.gooddata.com. Open a report that is populated by the process.
- From Data Integration Console, click Projects to open the Projects page.
- Click the name of the project that the schedule belongs to, and click the schedule to open it.
Depending on the type of the schedule you are running, one of the following happens:
If you are running an Automated Data Distribution (ADD) schedule for the datasets with the preselected incremental load mode, you are prompted to confirm the mode, or override it with full load mode (which will completely overwrite data in the datasets) or with loading data for a specific time interval. Confirm your choice.
The schedule is queued for execution and is run as platform resources are available.
If you are running a regular (non ADD) schedule, the schedule is queued for execution and is run as platform resources are available.
Stop Schedule Execution
To stop a schedule in the middle of execution, click Stop.
When an upload is stopped in the middle of execution, the data that has already been uploaded to the project remains in the project. If you are unsure whether your ETL process can safely resume loading data, you can manually delete the uploaded data from the Manage page of your project.