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Trifacta Dataprep


If you licensed Dataprep by Trifacta before Oct. 14, 2020, you are using the Dataprep by Trifacta Legacy product edition. On October 14, 2022, this product edition will be decommissioned by Google and will be no longer available for use. Current customers of this product edition are encouraged to transition to one of the product editions hosted by Trifacta. See Product Editions.


Aggregate functions perform a computation against a set of values to generate a single result. For example, you could use an aggregate function to compute the average (mean) order over a period of time. Aggregations can be applied as standard functions or used as part of a transformation step to reshape the data.

Aggregate across an entire column:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula average(Scores)

Output: Generates a new column containing the average of all values in the Scores column.

Transformation Name Pivot columns
Parameter: Values average(Score)
Parameter: Max number of columns to create 1

Output: Generates a single-column table with a single value, which contains the average of all values in the Scores column. The limit defines the maximum number of columns that can be generated.

NOTE: When aggregate functions are applied as part of a pivot transformation, they typically involve multiple parameters as part of an operation to reshape the dataset. See below.

Aggregate across groups of values within a column:

Aggregate functions can be used with the pivot transformation to change the structure of your data. Example:

Transformation Name Pivot columns
Parameter: Row labels StudentId
Parameter: Values average(Score)
Parameter: Max number of columns to create 1

In the above instance, the resulting dataset contains two columns:

  • studentId - one row for each distinct student ID value
  • average_Scores - average score by each student (studentId)

NOTE: You cannot use aggregate functions inside of conditionals that evaluate to true or false.

A pivot transformation can include multiple aggregate functions and group columns from the pre-aggregate dataset. See Pivot Transform.

NOTE: Null values are ignored as inputs to these functions.

 These aggregate functions are available:

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