In the

Name Requirements

• Column names are case-insensitive and cannot begin with whitespace.
• Column names cannot contain escaped characters, such as \n.

column1,{Column 2 with space},column3

Reserved keywords

The following keywords should not be used as column names, as they may conflict with underlying requirements of the platform or the running environments with which it integrates:

• TRIFACTA__LINEAGE_INFO 

• TRIFACTA__FILE_LINEAGE_INFO 

  Info
  NOTE: There are two underscore characters in a row (__) after TRIFACTA in each of the above entries.

Rename Individual Columns

Rename a column through column menu

To rename a column, click the drop-down caret next to the column name. Click Rename.

Rename a column through suggestions

Steps:

1. If your column already exists, click the name of the column. 
2. Click the Rename suggestion card.
3. Click Modify.
4. Replace the newColumnName value with your preferred column name.

Rename a column through transformation

You can use the following transformation to rename a single column through the Transform Builder. In this case, the Rename columns transformation is used to perform a manual rename of MySourceCol to MyNewCol.

Rename a new column

Columns that are generated through transform steps are given a default name. 

For the following types of transforms, however, you can specify the column name as part of the step:

• derive
• extractkv
• merge
• nest

When a transform is added to the recipe, an as: clause is automatically added to the transform step. You can modify your transform to change the value of the as: column. For example, the following transform generates a new column with the first word from the Name column. The as: value renames this generated column as FirstName:

Auto-Generated Column Names

When your transforms generate new columns, names are automatically assigned to these columns based on the following pattern. 

1. If the transform includes a function reference, the function name is included in the new column. Example:

   D trans
   Type step p01Name Formula type p01Value Single row formula p02Name Formula p02Value LEFT(city,3) SearchTerm New formula

   
   
   New column name: left_city

2. If the above step is applied again, a duplicate column is generated with the following name. Example:

   D trans
   Type step p01Name Formula type p01Value Single row formula p02Name Formula p02Value LEFT(city,3) SearchTerm New formula

   
   
   New column name: left_city1

3. If the transform does not contain a function reference, the following convention is used:
   
   

   D trans
   Type step p01Name Formula type p01Value Single row formula p02Name Formula p02Value 'A' SearchTerm New formula

   New column name: column1



   D trans
   Type step p01Name Formula type p01Value Single row formula p02Name Formula p02Value 'B' SearchTerm New formula

   New column name: column2

Rename Multiple Columns

Steps:

1. Open the Transform Builder to add a new step to your recipe. 
2. From the drop-down in the first textbox, select Rename columns.
3. Select your method of renaming. See below.

4. Select the column or columns to which to apply the rename.

   Tip
   Tip: To apply the renaming across all columns in the dataset, select All. This option is useful for pattern-based renames, such as adding a prefix or changing case.

5. To add the step to your recipe, click Add.

Manual rename multiple columns

For each column that you select, you must add the new name just below the old one. 

• To add additional columns to the mapping, click Add.
• To remove columns from the mapping, click Remove.

Add prefix

For the selected columns, you can apply a specific prefix value to the names.

Transformation:

Add suffix

For the selected columns, you can apply a specific suffix value to the names. Example:

Transformation:

Apply rename to all columns

The following transformation performs the same rename as the previous one. Instead, it uses the All option to apply the rename across all columns of the dataset. If the number of columns changes in the future, then the rename is still applied across all of the columns in the dataset.

Transformation:

Convert to lowercase

For the selected columns, you can convert the columns names to lowercase. Example:

Transformation:

For example, if the old column name is Sales_Type, then the new column name is renamed to sales_type.

Convert to UPPERCASE

For the selected columns, you can convert the columns names to uppercase. Example:

Transformation:

For example, if the old column name is Sales_Type, then the new column name is renamed to SALES_TYPE.

Keep from beginning (left)

For the selected columns, you can specify the number of characters to keep from the beginning (left) of the column names.  Based on the number of characters you provide, the column name is updated. Example:

Transformation:

For example, if the old column name is Sales_Type, then based on the number of characters to keep from the beginning (left) is 3, then new column name is renamed to Sal.

Keep from end (right)

For the selected columns, you can specify the number of characters to keep from end (right) of the column names.  Based on the number of characters you provide, the column name is updated. Example:

Transformation:

For example, if the old column name is Sales_Type, then based on the number of characters to keep from the end (right) is 4, then new column name is renamed to Type.

Find and replace

You can apply literals, 

For the selected columns, you can specify the number of characters to keep from end (right) of the column names.  Based on the number of characters you provide, the column name is updated. Example:

Transformation:

The above uses literal values for find and replace. For more information on pattern-based matching, see Text Matching.

Use row(s) as column names

When this method is applied, all of the values in the specified row or rows are used as the new names for each column.

Types:

Source row number information:

• If a value is not applied for the source row number, the next row of data is used.
• Source row numbers apply. Current row numbers may not be the same. In the data grid, mouse over the leftmost column to see available row information.
• Each value in the row or combination of values across rows must be unique within the set of new column names.
• The row is removed from its original position.
• If the product is unable to find unique multi-row headers for the column, the first row of the header set is used.

Combine multiple rows

The following transformation renames the columns in the dataset based on the values in rows 3 and 4 of the data:

In the above:

• The separator is defined as an underscore character (_). This value can be empty.
• When Fill across is selected, if any row value is empty, the last non-empty value for the row in a previous column is used as part of the column header.