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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.

Unpacks array data into separate rows for each value. This transform operates on a single column.

This transform does not reference keys in the array. If your array data contains keys, use the unnest transform. See Unnest Transform.

Basic Usage

flatten col: myArray

Output: Generates a separate row for each value in the array. Values of other columns in generated rows are copied from the source. 

Syntax and Parameters

flatten: col: column_ref

TokenRequired?Data TypeDescription
flattenYtransformName of the transform
colYstringSource column name 

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


Identifies the column to which to apply the transform. You can specify only one column.

Usage Notes:

Required?Data Type
YesString (column name)


Tip: For additional examples, see Common Tasks.

Example - Flatten an array

In this example, the source data includes an array of scores that need to broken out into separate rows.




When the data is imported, you might have to re-type the Scores column as an array:

Transformation Name Change column data type
Parameter: Columns Scores
Parameter: New type Array

You can now flatten the Scores column data into separate rows:

Transformation Name Expand Array into rows
Parameter: Column Scores



This example is extended below.

Example - Flatten and unnest together

While the above example nicely flattens out your data, there are two potential problems with the results:

  • There is no identifier for each test. For example, Allen Adams' score of 87 cannot be associated with the specific test on which he recorded the score.
  • There is no unique identifier for each row.

The following example addresses both of these issues. It also demonstrates differences between the  unnest and the flatten transform, including how you use  unnest to flatten array data based on specified keys.


You have the following data on student test scores. Scores on individual scores are stored in the Scores array, and you need to be able to track each test on a uniquely identifiable row. This example has two goals:

  1. One row for each student test
  2. Unique identifier for each student-score combination




When the data is imported from CSV format, you must add a header transform and remove the quotes from the Scores column:

Transformation Name Rename column with row(s)
Parameter: Option Use row(s) as column names
Parameter: Type Use a single row to name columns
Parameter: Row number 1

Transformation Name Replace text or pattern
Parameter: Column colScores
Parameter: Find '\"'
Parameter: Replace with ''
Parameter: Match all occurrences true

Validate test date: To begin, you might want to check to see if you have the proper number of test scores for each student. You can use the following transform to calculate the difference between the expected number of elements in the Scores array (4) and the actual number:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula (4 - arraylen(Scores))
Parameter: New column name 'numMissingTests'

When the transform is previewed, you can see in the sample dataset that all tests are included. You might or might not want to include this column in the final dataset, as you might identify missing tests when the recipe is run at scale.

Unique row identifier: The Scores array must be broken out into individual rows for each test. However, there is no unique identifier for the row to track individual tests. In theory, you could use the combination of LastName-FirstName-Scores values to do so, but if a student recorded the same score twice, your dataset has duplicate rows. In the following transform, you create a parallel array called Tests, which contains an index array for the number of values in the Scores column. Index values start at 0:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula range(0,arraylen(Scores))
Parameter: New column name 'Tests'

Also, we will want to create an identifier for the source row using the sourcerownumber function:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula sourcerownumber()
Parameter: New column name 'orderIndex'

One row for each student test: Your data should look like the following:


Now, you want to bring together the Tests and Scores arrays into a single nested array using the arrayzip function:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula arrayzip([Tests,Scores])

Your dataset has been changed:


Use the following to unpack the nested array:

Transformation Name Expand arrays to rows
Parameter: Column column1

Each test-score combination is now broken out into a separate row. The nested Test-Score combinations must be broken out into separate columns using the following:

Transformation Name Unnest Objects into columns
Parameter: Column column1
Parameter: Paths to elements '[0]','[1]'

After you delete column1, which is no longer needed you should rename the two generated columns:

Transformation Name Rename columns
Parameter: Option Manual rename
Parameter: Column column_0
Parameter: New column name 'TestNum'

Transformation Name Rename columns
Parameter: Option Manual rename
Parameter: Column column_1
Parameter: New column name 'TestScore'

Unique row identifier: You can do one more step to create unique test identifiers, which identify the specific test for each student. The following uses the original row identifier OrderIndex as an identifier for the student and the TestNumber value to create the TestId column value:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula (orderIndex * 10) + TestNum
Parameter: New column name 'TestId'

The above are integer values. To make your identifiers look prettier, you might add the following:

Transformation Name Merge columns
Parameter: Columns 'TestId00','TestId'

Extending: You might want to generate some summary statistical information on this dataset. For example, you might be interested in calculating each student's average test score. This step requires figuring out how to properly group the test values. In this case, you cannot group by the LastName value, and when executed at scale, there might be collisions between first names when this recipe is run at scale. So, you might need to create a kind of primary key using the following:

Transformation Name Merge columns
Parameter: Columns 'LastName','FirstName'
Parameter: Separator '-'
Parameter: New column name 'studentId'

You can now use this as a grouping parameter for your calculation:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula average(TestScore)
Parameter: Group rows by studentId
Parameter: New column name 'avg_TestScore'


After you delete unnecessary columns and move your columns around, the dataset should look like the following:


See Also for Flatten Transform:

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