is the domain-specific language used to build transformation recipes in .

A  recipe is a sequence of transformation steps applied to your dataset in order to produce your results.

Transform and transformation:

For most of these actions, you can pass one or more parameters to define the context (columns, rows, or conditions).


Some parameters accept one or more functions. A function is a computational action performed on one or more columns of data in your dataset.

When you select suggestions in the Transformer Page, your selection is converted into a transformation that you can add to your recipe.

Tip: Where possible, you should make selections in the data grid to build transformation steps. These selections prompt a series of cards to be displayed. You can select different cards to specify a basic transformation for your selected data, choose a variant of that transformation, and then modify the underlying recipe as necessary.

 vs. SQL

NOTE: is not SQL. It is a proprietary language of data transformation, purpose-built for .

While there are some overlaps between  and SQL, here are the key distinctions:


 transforms follow this general syntax:

(transform) param1:(expression) param2:(expression)

Transform ElementDescription

In , a transform (or verb) is a single keyword that identifies the type of change you are applying to your dataset.

A transform is always the first keyword in a recipe step. Details are below.

The other elements in each step are contextual parameters for the transform. Some transforms do not require parameters.

parameter1:, parameter2:

Additional parameters may be optional or required for any transform. 

NOTE: A parameter is always followed by a colon. A parameter may appear only one time in a transform step.

Common Parameters

Depending on the transform, one or more of valuecol, and row parameters may be used. For example, the set transform can use all three or just value and col.

Transform ElementDescription

When present, the value parameter defines the expression that creates the output value or values stored when the transform is executed.

An expression can contain combinations of the following:

  • Functions apply computations or evaluations of source data, the outputs of which can become inputs to the column. Sources may be constants or column references. A function reference is always followed by brackets (), even if it takes no parameters. See below.
  • Operators are single-character representations of numeric functions, comparisons, or logical operators. For example, the plus sign (+) is the operator for the add function. See below.
  • Constants can be quoted string literals ('mystring'), Integer values (1001), Decimal values (1001.01), Boolean values (true or false) or patterns.

For more information on , see Text Matching.


When present, the col parameter identifies the name of the column or columns to which the transform is applied.

Some transforms may support multiple columns as a list, as a range of columns (e.g., column1~column5), or all columns in the dataset (using wildcard indicator, col: *).

row:When present, the row parameter defines the expression to evaluate to determine the rows on which to perform the transform. If the row expression evaluates to true for a row, the transform is performed on the row.

For aggregating transforms, such as window, pivot, and derive, the group parameter enables you to calculate aggregation functions within a group value. For example, you can sum sales for each rep by applying group:repName to your transformation.

order:For aggregating transforms, such as window , pivot , and derive , the order parameter can be used to specify the column by which the transform results are sorted. In the previous example, you might choose to sort your sum of sales calculation by state: order:State.

Parameter Inputs

The following types of parameter inputs may be referenced in a transform's parameters.

Other  can be referenced as column references. For literal values of these data types, you can insert them into your expressions as strings. Transforms cause the resulting values to be re-inferred for their data type.

Column reference

A reference to the values stored in a column in your dataset. Columns can be referenced by the plain-text value for the column name.

Example:  value parameter references the myCol column.

Column names with spaces or special characters in a transformation must be wrapped by curly braces. 

Example: Below, srcColumn is renamed to src Column, which requires no braces because the new name is captured as a string literal:

NOTE: Current column names that have a space in them must be bracketed in curly braces. The above column name reference is the following: {src Column}.


Some parameters accept functions as inputs. Where values or formulas are calculated, you can reference one of the dozens of functions available in .


Metadata variables

 supports the use of variable references to aspects of the source data or dataset. In the following example, the ABS function is applied to each column in a set of them using the $col reference. 

$col returns the value from the current column for the row under evaluation.For more information on these variables, see Source Metadata References.

Nested expressions

Individual parameters within a function can be computed expressions themselves. These nested expressions can be calculated using constants, other functions, and column references.

Example: Computes a column whose only value is ten divided by three, rounded to the nearest integer (3):


A valid integer value within the accepted range of values for the Integer datatype. 

Example: Generates a column called, my13 which is the sum of the Integer values 5 and 8:


A valid floating point value within the accepted range of values for the Decimal datatype. 

Example: Generates a column of values that computes the approximate circumference of the values in the diameter column:


A true or false value.

Example: If the value in the order column is more than 1,000,000, then the value in the bigOrder column is true.


A string literal value is the baseline datatype. String literals must be enclosed in single quotes.

Example: Creates a column called, StringCol containing the value myString.

supports a special syntax, which simplifies the generation of matching patterns for string values.

Patterns must be enclosed in accent marks ( `MyPattern`). For more information on , see Text Matching.Example: Extracts up to 10 values from the MyData column that match the basic pattern for social security numbers (XXX-XX-XXXX):

Regular expression

Regular expressions are a common standard for defining matching patterns. Regex is a very powerful tool but can be easily misconfigured.

Regular expressions must be enclosed in slashes ( /MyPattern/ ).

Example: Deletes all two-digit numbers from the qty column:


A valid date or time value that matches the requirements of the Datetime datatype.

Datetime values can be formatted with specific formatting strings. See DATEFORMAT Function.

Example: Generates a new column containing the values from the myDate column reformatted in yyyymmdd format:


A valid array of values matching the Array data type.



Example: Generates a column with the number of elements in the listed array (7):


A valid set of values matching the Object data type.



Example: Generates separate columns for each of the specified keys in the object ( brand, model, color), containing the corresponding value for each row:

For more information on these data types, see Supported Data Types.

Interactions between  and the Application

  1. As you build  steps in the Transform Builder, your syntax is validated for you. You cannot add steps containing invalid syntax. 
    1. Error messages are reported back to the application, so you can make immediate modifications to correct the issue.
    2. Type-ahead support can provide guidance to the supported transforms, functions, and column references.
  2. When you have entered a valid transform step, the results are previewed for you in the data grid.
    1. This preview is generated by applying the transformation to the sample in the data grid. 

      NOTE: The generated output applies only to the values displayed in the data grid. The function is applied across the entire dataset only during job execution.

    2. If the previewed transformation is invalid, the data grid is grayed out.
  3. When you add the transformation to your recipe: 
    1. It is applied to the sample in the application, and the data grid is updated to the current state.
    2. Column histograms are updated with new values and counts.
    3. Column data types may be re-inferred for affected columns.
  4. Making changes:
    1. You can edit any transformation step in your recipe whenever needed.
      1. When you edit a transformation step in your recipe, the context of the data grid is changed to display the state of your data up to the point of previewing the step you're editing. 
      2. All subsequent steps are still part of the recipe, but they are not applied to the sample yet.
      3. You can insert recipe steps between existing steps.
    2. When you delete a recipe step, the state remains at the point where the step was removed.
      1. You can insert a new step if needed.
      2. When you complete your edit, select the final step of the recipe, which displays the results of all of your transformation steps in the data grid. Your changes may cause some recipe steps to become invalid.

For more information on these areas of the , see Transformer Page.


A transformation is an action for which you can browse or search through the Transform Builder in the Transformer page. When specified and added to the recipe, these sometimes complex actions are rendered in the recipe as steps using the underlying transforms of the language. 

Tip: Through transformations, guides you through creation of more sophisticated steps that would be difficult to create in raw .  

For more information on the list of available transformations, see Transformation Reference.

For more information on creating transformation steps in the Transformer page, see Transform Builder.


A function is an action that is applied to a set of values as part of a transform step. Functions can apply to the values in a transform for specific data types, such as strings, or to types of transforms, such as aggregate and window function categories. A function cannot be applied to data without a transform.

Function input parameters

Below, function inputs are listed in increasing order of generality.

NOTE: A function cannot take a higher-order parameter input type without taking the lower parameter input types. For example, a function cannot take a nested function as an input if it does not accept a literal value, too.

OrderParameter input typeExample
FUNCTION('my input')

Function categories

Function CategoryDescription

Aggregate Functions

These functions are used to perform aggregation calculations on your data, such as sum, mean, and standard deviation.
Comparison FunctionsComparison functions enable evaluation between two data elements, which are typically nested (Object or Array) elements.
Math FunctionsPerform computations on your data using a variety of math functions and numeric operators.
Trigonometry FunctionsCalculate standard trigonometry functions as well as arc versions of them.
Date FunctionsUse these functions to extract data from or perform operations on objects of Datetime data type.
String FunctionsManipulate strings, including finding sub-strings within a string.
Nested FunctionsThese functions are designed specifically to assist in wrangling nested data, such as Objects, Arrays, or JSON elements.
Type FunctionsUse the Type functions to identify valid, missing, mismatched, and null values.
Window FunctionsThe Window functions enable you to perform calculations on relative windows of data within your dataset.
Other FunctionsMiscellaneous functions that do not fit into the other categories 

Operator Categories

An operator is a single character that represents an arithmetic function. For example, the Plus sign (+) represents the add function. 

Operator CategoryDescription
Logical Operatorsand, or, and not operators
Numeric OperatorsAdd, subtract, multiply, and divide
Comparison OperatorsCompare two values with greater than, equals, not equals, and less than operators
Ternary OperatorsUse ternary operators to create if/then/else logic in your transforms.


 transform, or verb, is an action applied to rows or columns of your data. Transforms are the essential set of changes that you can apply to your dataset.

Transforms are described in the Language Appendices. For more information, see Transforms.


Documentation for  is also available through . Select Resources menu > Documentation.

Tip: When searching for examples of functions, try using the following form for your search terms within the Product Documentation site:

  • Functions: wrangle_function_NameOfFunction

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