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Deduplicate Data

As part of your data cleansing steps, you might need to remove duplicate rows of data from your dataset.

Validate Duplicate Data

In some cases, it might be acceptable to have duplicated data. For example, additional records using the same primary key might be included in a dataset as amendments or detail records.

Nota

Before you remove duplicates from your dataset, you should verify that the data should not contain duplicates at all. If the data structure supports some duplicate elements including key values, you should exercise care in how you identify what constitutes duplicate information.

Remove duplicate rows transformation

Designer Cloud provides a single transformation, which can remove identical rows from your dataset:

Dica

If you are attempting to identify if there are duplicate rows, check the row count in your dataset before and after you have added this transformation.

Transformation Name

Remove duplicate rows

Limitations:

  • This transformation is case-sensitive. So, if a column has values Hello and HELLO, the rows containing those values are not considered duplicates and cannot be removed with this transformation.

  • Whitespace and the beginning and ending of values is not ignored.

Before applying the Remove deduplicate rows transformation, you should attempt to normalize your data. You can use the following techniques to normalize a few columns of data.

Nota

If you have more than 20 columns of data, you might be better served by trying to identify a primary key method for de-duplicating your dataset. Details are below.

For individual columns, you can use the trim function to remove leading and trailing whitespace:

Nota

To preserve the original column values, use the New formula transformation. The Edit column with formula transformation replaces the original values.

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

TRIM(Item)

Since the Remove deduplicate rows transformation is case-sensitive, you can use the LOWER function to make the case of each entry in a column to be consistent:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

LOWER(Description)

Deduplicate Rows Based on a Primary Key

Another method to deduplicate data might be to delete rows based on one or more columns that you identify as a primary key for the dataset. A primary key is an identifier that uniquely identifies a row of data within a dataset. It can be a single field (column) or a combination of columns. For example, in a datasets of restaurant locations, the primary key can be a combination of RestaurantName, Address, and Zip.

Nota

Before continuing, you must identify a primary key for your dataset. See Generate Primary Keys.

When you have identified your primary key, you should identify the appropriate method for your dataset. Please complete the following steps.

Steps:

  1. If your primary key spans multiple columns, use the Merge columns transformation to bring the values into a single column:

    Transformation Name

    Merge columns

    Parameter: Columns

    RestaurantName,Address,Zip

    Parameter: Separator

    '-'

  2. Rename the generated column: PrimaryKey.

  3. Use the following transformation to generate a new column, comparing each value in the PrimaryKey column to the previous one:

    Transformation Name

    Window

    Parameter: Formulas

    PREV(PrimaryKey, 1)

    Parameter: Order by

    PrimaryKey

  4. For each row, the value of the new column is the value in the PrimaryKey for the previous row. Now, test if this value is the same as the value in the PrimaryKey column for the current row:

    Transformation Name

    New formula

    Parameter: Formula type

    Single row formula

    Parameter: Formula

    IF((window==PrimaryKey),true,false)

    Parameter: New column name

    IsDupe

  5. The new column (IsDupe) contains true for duplicate primary keys. Delete the rows that are duplicates:

    Transformation Name

    Delete rows

  6. Delete any generated columns that are no longer needed.

Deduplicate Columns

While this form of duplicate data is rarer, you might want to check on the possibility of duplicate data between your columns. To check for duplicate column data, you can use a transformation similar to the following:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

Column1 == Column2

Parameter: New column name

'dupeColVals'

In the generated column, values that are true indicate duplicate data. If all values are true, then you can remove one of the columns.

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