prompts you to select an asset type. When you select Parse,
Data Quality
opens a new parse asset.
The asset displays a
Definition
tab and a
Configuration
tab. Use the Definition tab to define the name and the location of the asset. Use the Configuration tab to configure the steps that the mapping will apply to the input data.
You can also open the Definition and Configuration tabs when you open the asset from the Explore page.
To create a parse asset, click
New
Parse
.
On the Definition tab, enter a name for the parse asset.
Optionally, enter a description.
Select the location in which to save the parse asset.
Optionally, select a data quality dimension to represent the type of data quality issue that you want the asset to examine.
panel shows additional assets that are referenced by Data Quality assets to perform transformation tasks. When you create a new asset, there are no referenced assets.
Optionally, add a tag to the parse asset. You can search for assets with a common tag on the Explore page.
Select the Configuration tab.
Data Quality
displays the configuration workspace for the parse asset.
Data Quality
also displays a validation message to indicate that the step configuration is incomplete.
Select the parsing mode to apply to your data.
Select one of the following modes:
Custom. Select the custom mode to define one or more parsing steps with a dictionary or a regular expression.
Pre-built. Select the pre-built mode to use the built-in parsing logic in the asset.
Complete step 9 or step 10, based on your selection of Custom or Pre-built mode.
In Custom parsing mode, configure one or more steps in the asset.
Select one or more of the following step types:
Regular expression. Use a regular expression to parse one or more data values in an input string to discrete outputs. You can enter a regular expression or select one from a list of built-in expressions.
Dictionary. Use a dictionary to parse one or more data values in an input string to one or more discrete outputs.
In Pre-built parsing mode, select the locale in which your input data originates and select the format in which the data is stored.
Optionally, test the data and use the test results to enhance the pattern logic.
You can repeat the testing process to fine-tune the pattern logic.
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