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Release 7.6.2




NOTE: Transforms are a part of the underlying language, which is not directly accessible to users. This content is maintained for reference purposes only. For more information on the user-accessible equivalent to transforms, see Transformation Reference.

Merges two or more columns in your dataset to create a new column of String type. Optionally, you can insert a delimiter between the merged values. 

NOTE: This transform applies to String columns or other columns that can be interpreted as strings (for example, Zip codes could be interpreted as five-digit strings). To concatenate arrays, use the ARRAYCONCAT function. See ARRAYCONCAT Function.

Basic Usage

Column example:

merge col:Column1,Column2 as:'MergedCol'

Output: Merges the contents of Column1 and Column2 in that order into a new column called MergedCol.

Column and string literal example:

merge col:'PID',ProdId with:'-'

Output: Merges the string PID and the values in ProdId together. The string and the value are separated by a dash. Example output value: PID-00123.

Syntax and Parameters

merge col:column_ref [with:string_literal_pattern] [as:'new_column_name']

TokenRequired?Data TypeDescription
mergeYtransformName of the transform
colYstringSource column name or names
withNstringString literal used in the new column as a separator between the merged column values
asNstringName of the newly generated column

For more information on syntax standards, see Language Documentation Syntax Notes.


Identifies columns or range of columns as source data for the transform. You must specify multiple columns.

To specify multiple columns:

  • Discrete column names are comma-separated. 
  • Values for column names are case-sensitive.

merge col: Prefix,Root,Suffix

Output: Merges the columns Prefix, Root, and Suffix in that order into a new column.

Usage Notes:

Required?Data Type
YesString (column name)


Merge Columns transformation: Specifies the delimiter between columns that are merged. If this parameter is not specified, no delimiter is applied.

Replace Text or Pattern transformation: Specifies the replacement value.

merge col: CustId,ProdId with:'-'

Output: Merges the columns CustId and ProdId into a new column with a dash (-) between the source values in the new column.

Usage Notes:

Required?Data Type
NoString (column name)


Name of the new column that is being generated. If the as parameter is not specified, a default name is used.

merge col: CustId,ProdId with:'-' as:'PrimaryKey'

Output: Merges the columns CustId and ProdId into a new column with a dash (-) between the source values in the new column. New column is named, PrimaryKey.

Usage Notes:

Required?Data Type
NoString (column name)


Tip: For additional examples, see Common Tasks.

Example - Merging date values

You have date information stored in multiple columns. You can merge columns together to form a single date value.





merge col:Month~Year with:'/' as:'Date'


When you add the transform and move the generated Date column, your dataset should look like the following. Note that the generated column is automatically inferred as Datetime values.


Example - Use merge and settype to clean up numeric data that should be treated as other data types

This example illustrates how to clean up data that has been interpreted as numeric in nature, when it is actually String or a structured string type, such as Gender. This example uses:


The following example contains customer ID and Zip code information in two columns. When this data is loaded into the Transformer page, it is initially interpreted as numeric, since it contains all numerals.

The four-digit ZipCode values should have five digits, with a 0 in front.



CustId column: This column needs to be retyped as String values. You can set the column data type to String through the column drop-down, which is rendered as the following transformation:

Transformation Name Change column data type
Parameter: Columns CustId
Parameter: New type String

While the column is now of String type, future transformations might cause it to be re-inferred as Integer values. To protect against this possibility, you might want to add a marker at the front of the string. This marker should be removed prior to execution.

The basic method is to create a new column containing the customer ID marker (C) and then merge this column and the existing CustId column together. It's useful to add such an indicator to the front in case the customer identifier is a numeric value that could be confused with other numeric values. Also, this merge step forces the value to be interpreted as a String value, which is more appropriate for an identifier.

Transformation Name Merge columns
Parameter: Columns 'C',CustId

You can now delete the CustId columns and rename the new column as CustId.

ZipCode column: This column needs to be converted to valid Zip Code values. For ease of use, this column should be of type String:

Transformation Name Change column data type
Parameter: Columns ZipCode
Parameter: New type Zipcode

The transformation below changes the value in the ZipCode column if the length of the value is four in any row. The new value is the original value prepended with the numeral 0:

Transformation Name Edit column with formula
Parameter: Columns ZipCode
Parameter: Formula if(len($col) == 4, merge(['0',$col]), $col)

This column might now be re-typed as Zipcode type.



Remember to remove the C marker from the CustId column. Select the C value in the CustId column and choose the replace transform. You might need to re-type the cleaned data as String data.

See Also for Merge Transform:

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