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target is the set of columns, their order, and their formats to which you are attempting to wrangle your dataset. This target can be defined through imported or created datasets and must be assigned to an existing recipe.  After it is assigned to a recipe, a target appears in the Transformer page to assist in your wrangling efforts. You can also apply changes to selected columns based on the target.

  • This feature was formerly known as, "target matching."


  • Names of columns
  • Order of columns
  • Column data types
  • Data type format
  • Example rows of data

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 (or schema tag) is displayed.

Targets in the platform


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, a target is created from the information in a dataset and can be applied to a recipe in a flow. When you are working with the flow, the target information is available as your target for your wrangling activities, so that you can match up columns in your dataset (source) with their corresponding columns in the target. As you make changes in your recipe through the Transformer page, the target schema is available as a reference to see if your latest changes get you closer to matching the dataset to the target.


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.

Known Limitations

  • 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 settype transform to any selected column. No pattern matching or standardization is applied. For more information, see Overview of Pattern Matching.
  • Changes to the underlying objects of a target schema are not reflected in the schema. A target schema is a snapshot of the source object at the time of its creation. You cannot modify a target schema within the product. You must delete it and recreate itTo update, delete the target and create a new one


    Tip: If your target schema source is a recipe, then you can modify the recipe as needed and use it as your target again.


  • Output of a recipe in the same flow
  • A reference dataset from another flow
  • An imported dataset

    InfoNOTE: 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:


  • 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
  • Export Results WindowJob Details Page: After you have exported results and then imported them successfully into the target systemsuccessfully run a job, you can create a new dataset from the Export Results windowOutput Destinations tab. Through Flow View, this imported dataset can be used as the schema for wrangling. See Export Results WindowJob Details Page.

For more information, see Create Target.