Rename Columns

Because the names of the columns are the keys under which the data will be imported to Synerise or appear in the output file, you can change them to meet the requirements by using the Rename column transformation step.

In the settings of this step, you must select the columns that will be renamed. You can do this by selecting particular columns or you can create a condition that the column names must meet in order for the transformation to be performed on them, for example, the column name must start with the letter "A".

Step configuration

1. In the Data Transformation Studio, add the Rename column step.
2. Indicate the columns to rename.
3. From the Rename by dropdown list, select one of the following options:
   • Replacing - Select this option if you want to entirely change the column name, for example, from type to promotion_type
   • Adding prefix - Select this option if you want to add a group of letters at the beginning of a column name, for example, applying a promotion prefix to the Name column transforms it into promotionName
   • Adding suffix - Select this option if you want to add a group of letters to the end of a column name, for example, applying a .promotion suffix to the code column transforms it into code.promotion
4. In the field next to the dropdown list, enter the value.
5. To add more rules, click Add rule and repeat steps 2-4.
6. To define what happens if rows contain errors, from the dropdown list next to Add rule, select one of the error handling options.
7. Confirm the settings by clicking Apply.
8. You can check the result in the Output tab.

Selecting columns

In each rule of the step, you indicate the columns on which the operation is performed.

1. In the header of the rule, click these and select one of the options:
   • these - the operation is performed on the columns you indicate.
   • all except these - the operation is performed on all columns except the ones you indicate.
2. From the first dropdown list in the rule, select how you want to indicate the columns:
   • Columns - from the Select column dropdown list, select one or more columns.
   • Where name - define a condition (a dynamic condition) that the column names must meet. Select a logical operator (Ends with, Starts with, Contain, Not contain, or Regex) and enter the value. For example, if you select Starts with and enter pro, the operation is performed on the columns whose names start with pro
     Column names are case-sensitive, for example, product doesn't match ProductCode. A regular expression must match the entire column name, for example, product.* matches productName, but product doesn't. To ignore the letter case in a regular expression, add (?i) at the beginning, for example, (?i)product.*

You can't combine dynamic conditions with the all except these option.

Dynamic conditions are especially helpful when you use a JSON file as sample data. When a JSON file contains an object deeper than the root object, its items are transferred to the transformation as separate columns named {object}.{parameter}. You can't add those columns from the Select column list, but you can include them with a dynamic condition, for example, by including columns whose names contain the name of the object they were created from.

Handle errors

You can define what happens with rows in which the step fails (for example, as a result of Jinja rendering). From the dropdown list next to Add rule, select one of the following options:

• Skip row if error occurred - It omits the row when an exception occurs.
• Skip if result is null or empty string - It omits rows with empty strings and null (which may be the result of rendering Jinja).
• Stop further transformation - The transformation stops at the row with an error. The data before the line that caused the error is transformed (and imported if the transformation is used in the Data Transformation Studio node).
• Insert null if error occurred - Wrong values are replaced with null. If you use such a transformation for the import of profiles, the existing value of an attribute is cleared and replaced with the null value.
• Insert empty string if error occurred - Wrong value is removed and the cell remains empty.

Example of use

You can replace the email_address column name with email, which is a required column name for Synerise imports. As a result, the data from this column will be imported as the email attribute.

name,surname,email_address Dwight,Addams,dwight.addams@test.com Martin,Smith,martin.smith@test.com Kurt,Doe,kurt.doe@test.com

1. In the Data Transformation Studio, add sample data.
2. Click Add step and select Rename column.
3. In the configuration of the step:
   1. From the Select column dropdown list, select email_address.
   2. Leave the Rename by dropdown list at default (Replacing).
   3. In the field next to the dropdown list, enter email
4. Confirm by clicking Apply.
   Result: In the Output tab, the email_address column is renamed to email