In this lesson, you further transform your data to apply business rules, then complete a lookup that uses an additional reference table.
Overview
When you completed Lesson 3.3, you viewed the output data to review your results. You realized that one of the columns in the GlobalCo_BillTo.ds data set contains extraneous data, and that the extraneous data is now in your output data set. Some of the values in the SETUP_DATE column contain a special handling code in addition to the date that the account was set up. These values must be separated into the appropriate columns before the data is combined with the WorldCo customer data.
To separate the values, you add transformation logic to extract the special handling code from the SETUP_DATE column into a separate column. You then add a lookup operation to look up the text description that corresponds to the code from the lookup table that you populated in Lesson 2.3. You write this description to the output data, and also add a row count.
Tasks in this lesson
To complete multiple transformations in your job, add another Transformer stage and additional Lookup stages.

In the SOURCE column, the two-digit country code is prefaced by GlobalCo. Including this information is important so that developers can distinguish between the GlobalCo customer data and the WorldCo customer data when the cleansed data is merged.
The SPECIAL_HANDLING_CODE column contains a special handling code for each customer record. The corresponding description for each code is listed in the DESCRIPTION column. The shipping department at GlobalCo Worldwide can use this information to determine when each customer receives their delivery.
In this lesson, you applied stricter data typing to further transform and cleanse the GlobalCo billing data. To this point, you wrote your output data to data sets. In the following module, you write the output data to a relational database so that other developers in the GlobalCo Worldwide organization can easily access the data.