Concept: Dataset Example Data
When you're editing dataset transformations, you can view how your changes affect an example of the data. This view enables you to see how different stages impact your data.
You can change the data displayed by selecting any of these example options:
Example Option | Description | Security Requirements |
|---|---|---|
No Example | No data displayed. This option has the fastest load time. | Dataset Editor permission or better on the dataset. |
Default Example | Workday displays a subset of the data, enabling you to assess how different stages affect a subset of your data.
The example data request is canceled when you navigate away from the stage. | Dataset Editor permission or better on the dataset. |
Custom Example | This option is only available for derived datasets. You can create rules with different filter criteria for different subsets of your data. These rules enable you to curate what data you see and how each stage impacts it.
You can add 1 rule per base dataset or table used in the derived dataset. The rules you define are specific to this derived dataset and don’t affect other datasets with the same base dataset or table. When adding a filter stage, you can add more complex filter criteria similar to the Filter stage.
Only 1 request per tenant is allowed at a time, and the request timeout is set to 15 minutes. Click Refresh for Custom Example beside the example data control to retrieve current example data. |
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If you have permissions to view the
View Dataset Transformations
report, then you can change example data but won't be able to save. Workday displays up to 20,000 rows when you select either Default or Custom Example.
Comparing Example Data to Published Data
Workday displays example data on a limited number of records, which typically is less than the number of records published in a dataset. If the dataset uses a Join stage and 1 or more Prism calculated fields after the Join stage, the example data displayed might be different than the published data. The published data is correct because it works on all data from the sources.
Example: Your dataset includes a Join stage using a right outer join and a Prism calculated field with a CASE function that evaluates the value in a field from the secondary (right) pipeline. The example data might not find a match and return NULL for the field value, whereas the published data finds a match and returns a non-null value. As a result, the CASE function returns a different value in the example data than in the published data.