After completing this lesson, you'll be able to:
In this lesson, you will:
You might think removing attributes is a less critical task in FME. That's because - for a manual attribute schema - only attributes defined in the writer are written to the output; extra attributes not on the writer schema are dropped.
However, removing attributes has benefits:
You can unexpose attributes on reader feature types under the User Attributes tab. Unexposing attributes tidies your workspace, but it does not improve performance. However, some formats (primarily databases) also include an "Attributes to Read" parameter; using it will improve performance.
Transformers that can remove attributes are:
The AttributeManager and AttributeRemover use the same technique: select an attribute to remove.
In the AttributeManager, you can remove attributes by selecting them and clicking the - button. Alternatively, you can change the action field from Do Nothing to Remove.
Notice in the above screenshot that three attributes have been removed. The Output Attribute column is blank to signify that it is no longer present.
The AttributeKeeper transformer carries out the same function but approaches it from the opposite direction. It lets the user specify which attributes are not to be removed; in other words, this transformer lets the user specify which ones to keep.
So, you should use the AttributeManager when you want to remove one or two attributes but keep the rest. You should use the AttributeKeeper when you want to remove the majority of attributes and retain only one or two.
The BulkAttributeRemover - like the BulkAttributeRenamer - lets the user carry out a process on multiple attributes. In this case, instead of being able to select all attributes, the user enters a string-matching expression to define which attributes to remove:
Here, the user removes all attributes whose names end in the word "Count."
Refer to the RegEx Quick Reference below for help constructing regular expressions.

You can access AI Assist in any transformer that uses RegEx, SQL, or Python. AI Assist helps you build statements from natural language.
Want to check your work from the last exercise? Here are the transformers Jennifer chose:
- Remove
- BulkAttributeRemover
- AttributeRemover
- Rename
- AttributeRenamer
- BulkAttributeRenamer
- Create
- AttributeManager
- Label
- AttributeCreator
If you haven't done so already, please drag these transformers into their corresponding bookmark.



