A 

Transform and transformation:

• A transform is a single action applied to a dataset. A transform is part of the underlying 

  D s lang

  . Transforms are not directly accessible to users. 

• A transformation is a user-facing action that you can apply to your dataset through the Transformer page. A transformation is typically a use-specific or more sophisticated manifestation of a transform. 
  

  D s transforms

  Tip
  Tip: Except for the reference documentation for individual transforms, the language documentation references transformations that you can apply through the Transformer page.

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

Function:

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.

D s lang

 vs. SQL

While there are some overlaps between 

• D s lang

   is a proprietary language designed for data transformation. Every supported transformation is designed to make changes to a dataset. It cannot be used to read from or write to a datastore. 
  • Users interact with 

    D s lang

     exclusively through the

    D s webapp

    . There is no direct interaction with the language.
• SQL (Structured Query Language) is designed for querying, transforming, and writing for relational datasources. It cannot be applied to file-based datasets.
  • SQL cannot be used to transform data in 

    D s product

    .

D s lang

 Syntax

Common Parameters

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

Flow parameters

At the flow level, you can define parameters that can be referenced in your recipe steps. 

In the following transformation, the flow parameter currentDiscount is invoked in the step to yield the discounted cost. 

Flow parameters are referenced in your steps in the following format:

${MyRecipeParameter}

For more information, see Create Flow Parameter.

Parameter Inputs

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

Other 

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:

Functions

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

Example:

Metadata variables

$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):

Integer

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:

Decimal

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:

Boolean

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.

String

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.

D s item
pattern pattern

Patterns must be enclosed in accent marks ( `MyPattern`). For more information on

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:

Datetime

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:

Array

A valid array of values matching the Array data type.

Example:

[0,1,2,3,4,5,6,7,8]

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

Object

A valid set of values matching the Object data type.

Example:

{"brand":"Subaru","model":"Impreza","color","green"}

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

D s lang

 and the Application

1. As you build 

   D s lang

    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. 

      Info
      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

Transformation

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. 

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.

Functions

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.

FUNCTION('my input')

FUNCTION(myColumnOfValues)

FUNCTION(SUM(MyCol))

Function categories

Operator Categories

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

Transforms

A  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

Documentation for 

All Topics