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- This feature was formerly known as, "target matching."
In general, a target consists of the set of information required to define the expected data in a dataset. Often referred to as a "schema," this target schema information can include:
- Names of columns
- Order of columns
- Column data types
- Data type format
A dataset associated with a target is expected to conform to the requirements of the schema. Where there are differences between target schema and dataset schema, a validation indicator is displayed.
Targets in the platform
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NOTE: A target schema contains information on column names, column data types, and the order in which the columns are organized in the target. The length of individual columns is not maintained or enforced.
Targets are applied only after initial type inferencing has been applied to the loaded dataset.
Tip: As needed, you can disable initial type inferencing when data is imported into the product. See Import Data Page.
- Type-based matching applies a
settypetransform to any selected column. No pattern matching or standardization is applied. For more information, see Overview of Pattern Matching.
A target schema is a snapshot of the source at the time of creation. You cannot modify a target schema within the product. You must delete it and recreate it.
Tip: If your target schema source is a recipe, then you can modify the recipe as needed and use it as your target again.
Sources for creating a target
The schema used to define a target can be imported and assigned from any of the following objects, including:
- Output of a recipe in the same flow
- A reference dataset from another flow
An imported dataset
NOTE: Changes to the underlying objects of a target schema are not reflected in the schema. A target schema is a snapshot of the object at the time of its creation. To update, delete the target and create a new one. For more information, see Create Target.
Ideally, the source of the target schema should come from the publishing target. If you are publishing to a pre-existing target, you can create do one of the following:
- Reference the target: If the schema is represented in a dataset to which you have access in
, you can use it as your target schema.
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- Import the target: Import the target table or schematized source into
as an imported dataset. Then, it can be selected as the target schema for any recipe to which you have access. See Import Data Page.
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- Extract target to a supported format: If you cannot import the target directly into
, you could create an extract of a few rows, including the header, for the target into one of the formats supported for import. For more information, see Supported File Formats.
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Creating a target for a recipe
You can create a target through one of the following mechanisms:
- Flow View: Select a recipe. From the context menu in the right panel, select Assign Target to Recipe. See Flow View Page.
- Transformer Page: Above the data grid, click the Target icon and select Attach a new Target.
- Job Details Page: After you have successfully run a job, you can create a new dataset from the Output Destinations tab. Through Flow View, this imported dataset can be used as the schema for wrangling. See Job Details Page.
For more information, see Create Target.
Using a target
After a target has been attached to a recipe, the target schema appears in a toolbar above the data grid along with a preview of the data. You can then make modifications to the data so that each column matches the definition for the corresponding column in the schema. See Data Grid Panel.
Through the data grid and the Column Browser, you can perform operations on selected columns in your dataset to align them with the target schema. For more information, see Column Browser Panel.
Running jobs on recipes with assigned targets
NOTE: You can run a job even if there are differences between the schema and your dataset. In