Use a Data Validation
transformation to improve the quality of operational data before you
load that data into a data warehouse or data mart. You can detect
error conditions and specify actions that alleviate those errors.
Error conditions include blank or missing values, duplicate values,
and invalid values. The actions that you can take in response to erroneous
values include stopping the job, changing the value, or writing the
row to an error table instead of to the target.
Custom validation enables
you to apply source values to user-written expressions. You then define
the actions that are taken in response to true and false results.
Custom actions include the replacement of source values in the target.
Replacement values can be generated by a second expression, or they
can be obtained from a translation table.
Each of the validation
actions sends information to an exception report, which you can create
on the
Error and Exception Tables tab. You
can specify the name and path of the exception report on the
Status
Handling tab.