When data is imported:
- Supported data types from the source are converted to corresponding data types supported by the application, based upon the conversions listed in this section.
- Types that are not supported but are recognized by the application are converted to String types.
- Data for types that cannot be read from the source due to technical reasons are converted to null values on import.
By default, the Cloud Dataprep application 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 Cloud Dataprep data types 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 20-25 rows of data to work consistently.
- If your dataset has fewer than 20 rows, type inference may not have sufficient data to properly infer the column type.
In some datasets, the first 20-25 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:
- The data is written out from Cloud Dataprep by TRIFACTA INC. as Integer data type. This works for the first 25 rows.
- The other 2,000 rows are written out as null values, since they do not match the Integer data type. If the source data used decimal notation (e.g.
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.
- If all input values are double-quoted, then Cloud Dataprep by TRIFACTA INC. evaluates all columns as String type. As a result, type inference cannot be applied. Since non-String data types cannot be inferred, then the first row cannot be detected as anomalous against the inferred type (String). Column headers cannot be automatically detected from double-quoted source files.
- After data has been imported, you can remap individual column types through recipe steps. For more information, see Change Column Data Type.
On export from the Cloud Dataprep application:
- The application maps the internal Cloud Dataprep data type to the explicit type listed in the appropriate page in this section.
- Unmapped types are converted to the equivalent of strings.
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.Cloud Dataprep data types:
Supported Data Types
|String Data Type||Any non-null value can be typed as String. A String can be anything.|
|Integer Data Type||The Integer data type applies to positive and negative numeric values that have no decimal point.|
|Decimal Data Type||
Decimal data type applies to floating points up to 15 digits in length.
|Boolean Data Type||The Boolean data type expresses true or false values.|
|Social Security Number Data Type||This data type is applied to numeric data following the pattern for United States Social Security numbers.|
|Phone Number Data Type||This data type is applied to numeric data following common patterns that express telephone numbers.|
|Email Address Data Type||This data type matches text values that are properly formatted email addresses.|
|Credit Card Data Type||Credit card numbers are numeric data that follow the 16-digit pattern for credit cards.|
|Gender Data Type||This data type matches a variety of text patterns for expressing male/female distinctions.|
|Zip Code Data Type||This data type matches five- and nine-digit U.S. zipcode patterns.|
|State Data Type||State data type is applied to data that uses the full names or the two-letter abbreviations for states in the United States.|
|Object Data Type||An Object data type is a method for encoding key-value pairs. A single field value may contain one or more sets of key-value pairs.|
|Array Data Type||An array is a list of values grouped into a single value. An array may be of variable length; in one record the array field may contain two elements, while in the next record, it contains six elements.|
|IP Address Data Type||The IP Address data type supports IPv4 address.|
|URL Data Type||URL data type is applied to data that follows generalized patterns of URLs.|
|HTTP Code Data Type||Values of these data types are three-digit numeric values, which correspond to recognized HTTP Status Codes.|
|Datetime Data Type||Cloud Dataprep by TRIFACTA® INC. supports a variety of Datetime formats, each of which has additional variations to it.|
For more information on the data types that are supported within the Cloud Dataprep application, see Supported Data Types.