You need realistic data for testing in a nonproduction environment. You have production data that includes sensitive data columns. You cannot use the data without the risk of compromising sensitive data. Use the Data Masking transformation to mask sensitive data before you use the data in a test environment.
The production data includes a table Personnel_Information with the following data:
First Name
Surname
DOB
Address
State
Uma
Hilel
03/12/1985
24, Atkins Avenue
CA
John
Sen
07/15/1963
49, Wheeler Road
MN
Keiko
Burns
11/24/1989
13, Draker Drive
PA
Isadora
Buckley
08/16/1990
20, Fountain Center
CA
In the Mapping Designer, add Personnel as a source transformation for the table Personnel_Information. Add a target transformation Personnel_test.
Add the Data Masking transformation to the mapping canvas and connect it to the data flow.
You need to mask the Surname, DOB, and the State columns to ensure sensitive data is masked. You can use the Substitution Last Name masking technique to mask the Surname column. This masking technique replaces data in the column with data from the dictionary file on surnames. You can use the Random Date masking technique to mask the DOB column. Use the Substitution State masking technique to mask the State column. This masking technique replaces data in the column with data from the dictionary file on U.S. state names.
When the mapping run completes successfully, you can view the masked data in the Personnel_test output table :
First Name
Surname
DOB
Address
State
Uma
Acothley
05/12/1985
24, Atkins Avenue
Michigan
John
Mcgovern
04/15/1963
49, Wheeler Road
Oklahoma
Keiko
Garsia
03/24/1990
13, Draker Drive
Montana
Isadora
Sonnier
01/16/1991
20, Fountain Center
South Carolina
You can now use the masked data in the table Personnel_test in the test environment.
