Branding options in FME Server Apps let you customize the appearance, including the logo that appears in the user interface. You can replace the default FME logo with your own branding, or configure the app to display only your logo instead of the FME mark. This white-labeling capability is provided through the branding settings in the FME Server administration interface, allowing you to present a branded experience for your organization or clients. It’s a built-in feature and does not require a premium license, so removing or replacing the FME logo is supported as part of standard branding customization.

List attributes in FME store multiple values under one attribute on a single feature, representing a collection. This matches object-oriented thinking, where an object can have a property that is a list of items, and it also maps to relational thinking, where a one-to-many relationship is represented by multiple related rows. By collecting related items into a list attribute, you can represent the object-side structure, and by expanding that list into separate records, you can recreate the relational side. So, using list attributes enables translating between object-oriented and relational data models, making this statement true. For example, a single feature with a list of phone numbers can be mapped to separate phone number rows in a related table.

Schema drift shows up as differences between what a schema is supposed to be and what it currently is. SchemaScanner addresses this by comparing the active schema in your workspace with a reference or target schema and surfacing where names, types, or structures differ. The typical workflow is to run that comparison and then pass the results into a reporting or remediation step that highlights the differences. Those results can guide you to update the reference schema or adjust the data accordingly (for example, adapt field types, rename fields, or re-structure data) so subsequent processing stays in sync. It’s not about preventing drift upfront, invalidating submissions, or creating purely visualizations—the strength of SchemaScanner lies in producing a clear diff you can act on.

Join operations in FME hinge on join keys. A null or missing value on a join key cannot form a match, so in a strict inner join such features won’t obtain joined attributes and may be dropped. But you can configure the Joiner to keep unmatched features (left, right, or full outer join behavior) or to pass through features that don’t find a match, resulting in output where the joined attributes are empty for those features. Because of these options, features with null or missing join values are not always rejected—they can be preserved depending on the join type and settings.