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
• Integrated functionality allows the author to replace support transformers with tools built-into operational transformers
• 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

FME Skills

• The ability to locate a transformer to carry out a particular task, without knowing about that transformer in advance
• The ability to build strings and calculate arithmetic values using integrated tools
• 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

Further Reading

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