Module Review

This module was designed to introduce you to a wider range of FME transformers, plus a number of techniques for applying transformers more efficiently.

What You Should Have Learned from this Module

The following are key points to be learned from this session:

Theory

• There are distinct groups of transformers that do work other than transforming data attributes or geometry
• A large proportion of the most-used transformers are related to attribute-handling
• Filtering is the act of dividing data. Conditional Filtering is the act of dividing data on the basis of a test or condition
• Data Joins are carried out by transformers that merge data together, from within Workbench or from external data sources
• Integrated functionality allows the author to replace support transformers with tools built-into operational transformers

FME Skills

• The ability to locate a transformer to carry out a particular task, without knowing about that transformer in advance
• The ability to use common transformers for attribute management
• The ability to use transformers for filtering and dividing data
• The ability to use transformers for merging data together
• The ability to build strings and calculate arithmetic values using integrated tools

Further Reading

For further reading why not browse blog articles for particular transformers such as the TestFilter, AttributeCreator, or FeatureMerger?