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.

Deduplicate Transform

Designer Cloud Enterprise Edition provides a single transform, which can remove identical rows from your dataset:

Limitations:

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

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

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

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

For more information, see Normalize Numeric Values .

Deduplicate Rows Based on a Primary Key

An easier 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.

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 transform 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 transform 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 transform similar to the following:

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