Most Valuable Transformers
If you have a thorough understanding of the most common transformers, then you have a good chance of being a very efficient user of FME Workbench.
Anyone can be proficient in FME using only a handful of transformers if they are the right ones!
The Top 30
The list of transformers on the Safe Software website is ordered by most-used, calculated from user feedback. Having this information tells us where to direct our development efforts in making improvements, but it also gives users a head-start on knowing which of the (500+) FME transformers they’re most likely to need in their work.
The following table (last updated June 2018) provides the list of the most commonly used 30 transformers. The Tester transformer is consistently number one in the list every year, highlighting its importance.
Besides the obvious transformers for transforming geometry (Clipper, Bufferer, Dissolver) and the obvious transformers for transforming attribute values (StringReplacer, Counter) there are some other distinct groups of transformers.
|The FeatureJoiner is a new transformer for FME2018, designed to eventually replace the FeatureMerger.|
Workflow transformers - for example the FeatureReader - can be found in the Workflows category in the Navigator window. They are for managing the flow of data through a workspace. Sometimes they are used to read data, to write data, or to connect to particular web services. They also integrate with FME Server to submit jobs and notifications.
These transformers - mostly named the Attribute<Something> - are primarily for managing attributes (creating, renaming, and deleting) for schema mapping purposes. However, they can also be used to set new attribute values or update existing ones.
These transformers - mostly named the <Something>Filter - subdivide data as it flows through a workspace. Commonly the filter is a conditional filter, where the decision about which features are output to which connection is decided by some form of test or condition.
Joins are the opposite action to filtering; they are when separate streams of data are combined as they flow through a workspace. Like filtering, there is a condition to be met - in this case matching key values - that determine how and where the join takes place.