Transformers provide powerful ways to transform data as it moves from the source system to the destination system.
When the output from the source type or a transformer is connected to another transformer, Workbench makes an implicit connection between all attributes that have the same name. If necessary, Workbench automatically assigns attributes to transformers based on both the transformer itself (usually one specific attribute like count or length, for example) and then appends the attributes contained in the input connection.
Data Transformation is FME’s ability to restructure data. The key feature of this ability is that transformation automatically takes place between the reading (extract) and writing (load) of data, without requiring user intervention.
Data transformation can be subdivided into two distinct operations.
This type of transformation might be better called “reorganization”. It refers to the ability of FME to channel data from source to destination in an almost infinite number of arrangements. This would include the ability to merge data, divide data, reorder data, and define custom data structures.
Transforming the structure of a dataset is carried out by manipulation of its schema.
This type of transformation is also known as “restructuring”. It refers to the ability to alter the content of a dataset; manipulation of a feature’s geometry or attribute values is the best example of how FME is able to transform content.
If you are familiar with FME mapping files, the transformers provided in Workbench encapsulate the FME Functions and Factories. Transformers manipulate your source data to achieve the desired output. Just like functions and factories, some Some transformers add attributes to features, others erase attributes, and still others will only transform the geometry. Transformers can operate on individual features, one at a time, or on groups of features.
You can create and store your own Custom Transformers.
Workbench transformers are listed in the Transformer gallery.