Module Review

This module was designed to introduce you to a broader range of FME transformers, plus 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:

There are distinct groups of transformers that do work other than transforming data attributes or geometry
FeatureReader and FeatureWriter transformers read and write data at any point in a workspace
Integrated editing dialogs allow the author to replace transformers with built-in functions
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 using a test or condition
Data joins are carried out by transformers that merge data, from within Workbench or from external data sources

The ability to locate a transformer to carry out a particular task, without knowing about that transformer in advance
The ability to use FeatureReader and FeatureWriter transformers
The ability to read data from and write data to, web services
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