- 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
|D s product|
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.
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
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. Info
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: