When data is imported:
By default, the applies type inference for imported data. The application attempts to infer a column's appropriate data type in the application based on a review of the first lines in the sample.
NOTE: Mapping source data types to depends on a sufficient number of values that match the criteria of the internal data type. The mapping of import types to internal data types depends on the data.
Type inference needs a minimum of 25 rows of data in a column to work consistently.
In some datasets, the first 25 rows may be of a data type that is a subset of the best matching type. For example, if the first 25 rows in the initial same match the Integer data type, the column may be typed as Integer, even if the other 2,000 rows match for the Decimal data type. If the column data type is unmodified:
3.0in the source), then those values are written out as null values, too.
In this case, it may be easier to disable type inference for this dataset. See below.
Tip: If you are having trouble getting your imported dataset to map to expected data types, you can disable type inference for the individual dataset. For more information, see Import Data Page.
After data is imported, provides some mechanisms for applying stronger typecasting to the data. Example:
On export from the :
Tip: You can import a target schema to assist in lining up your columns with the expected target. For more information, see Overview of RapidTarget.
For more information on the data types that are supported within the , see Supported Data Types.