The FME Transformer Gallery is vast, with over 500 different options to choose from. Often, knowing how to best make use of certain transformers can be a challenge. To ring in the New Year, our panel of support specialists will be taking you through their insider tips & tricks with their favourite transformers. - Natalie, the Creator: For when you just need that certain something to initialize an API call or test a workflow and take your workspaces to the next level. - Dan, the FeatureReader: Get more in-depth with your data reading. Flexible and dynamic workflows are about to become that much easier. - Trent, the AttributeRemove & Attribute Keeper : A 2-for-1 special with the trick to deciding when to use one versus the other to save you the most time. - Natalie, the Creator: For when you just need that certain something to initialize an API call or test a workflow and take your workspaces to the next level. - Chris, the AttributeManager: Ever wonder what it takes to be ranked the #1 FME transformer? Learn how this transformer, commonly referred to as the Swiss Army knife of transformers, is loaded with great features to help you simplify your workspace. - Jovita, the AttributeFilter: For all those times when you want to split up your data into a schema that fits your needs, whether you’re at the start of your workflow, or just about to write out at the end. - Nampreet, the FeatureMerger: An atypical, but useful, tip on how to use it to merge attributes without a common key attribute. - Debbi, the Counter: Not as simple as 1-2-3! Use this surprisingly versatile transformer to perform group counts and even count features at different parts of workspaces. - Evie, the Clipper: Clip anything from vector, rasters, and even point clouds. A simple trick of breaking down large datasets can drastically reduce memory usage when clipping! Don’t miss the chance to join us & make it your New Years' resolution to whip your data into shape(files) with these 9 fantastic FME transformers.

1. Safer’s Tips & Tricks to Optimize
9 Favourite FME Transformers

2. Meet the Presenters
Natalie Clouthier Dan Minney Trent Kading Chris Berger
Jovita Chan Nampreet Singh Evie Lapalme
Debbi Leung

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What’s your favourite
transformer?

6. Agenda
● Tips & Tricks for:
○ Creator
○ FeatureReader
○ AttributeRemover/Keeper
○ AttributeManager
○ AttributeFilter
○ FeatureMerger
○ Counter
○ Clipper
● Resources & Q+A

7. Natalie Clouthier, Creator
Creator

8. Creator Transformer
● Creates user-defined feature(s)
which can have geometry
■ a something
● No input port, so not dependent on
other datasets
● Initiates other transformers which
require an input
● Create mock datasets for testing
■ a sample

9. Demo

10. Tips & Tricks
Be creative!
Add User Parameters and custom XML
to your created objects
Use anytime you need:
● a mock dataset (a.k.a that something)
● to initiate or test other transformers

11. Dan Minney, FeatureReader
FeatureReader

12. FeatureReader
● More than just a regular reader. . .
● Allows for flexible and dynamic
workflows
● Supports advanced filtering before
data is even read in - letting the
database do the work
● Spatial and non-spatial filtering
● All formats are supported!
● Sibling transformer - FeatureWriter

13. Demo

14. Trent Kading, AttributeRemover or
AttributeKeeper
AttributeRemover
AttributeKeeper

15. AttributeRemover
● Primarily used for the removal of
attributes, including lists from an
input feature

16. AttributeKeeper
● Primarily used to keep of
attributes, including lists from an
input feature
● Also can be used for the creation of
Bulk features.

17. Demo

18. Tips & Tricks
Easy way to keep or remove attributes
Remember sometimes the opposite of
what you want is the better transformer
Easiest way to create Bulk features to
improve downstream performance

19. Reminder:
Get your questions in!

20. Chris Berger, AttributeManager
AttributeManager

21. AttributeManager
● One transformer to rule them all
○ Add, rename, copy, delete,
re-order…
● Built-in Functions
○ FME Feature, String, Math,
Date
● Conditional Values
● Import/expose
● Adjacent feature
[Screenshot can go here]

22. Demo

23. Jovita Chan, AttributeFilter
AttributeFilter

24. AttributeFilter - When do I use what?
Use the AttributeFilter
● Limited unique string values
● Group data for specific processes
● Identify features that donʼt fit
Donʼt use the AttributeFilter
● Numeric data or ranges → Tester
● Output specific feature types →
Feature Type Fanout

25. Demo

26. Nampreet Singh, FeatureMerger
FeatureMerger

27. FeatureMerger
Merge attributes and geometries from
multiple datasets
= Combining different data sources
= Data Integration!
= FME
Join On common key attribute or
expression, but not always…

28. Demo

29. FeatureMerger
Tips & Tricks
Set Requestor =1 and Supplier = 1 in the Join On parameters to
join one Supplier feature to multiple Requestor features.
Enable Generate List parameters for multiple matches.
Resources
● Webinar Data Integration Basics: Merging & Joining Data
https://www.safe.com/webinars
● Documentation
https://www.safe.com/transformers/feature-merger

30. Debbi Leung, Counter
Counter

31. Counter
● On the surface, a simple
transformer that creates a numeric
attribute with incremented values.
● The Advanced Parameters allow
you to perform some not-so-simple
functionality such as:
○ Counting in groups
○ Using the same counter domain
(variable) at different points in the
workspace
○ Create a basic histogram for
values of an attribute

32. Counter
● On the surface, a simple
transformer that creates a numeric
attribute with incremented values.
● The Advanced Parameters allow
you to perform some not-so-simple
functionality such as:
○ Counting in groups
○ Using the same counter domain
(variable) at different points in the
workspace
○ Create a basic histogram for
values of an attribute

33. Demo

34. Tips & Tricks
Caution using partial runs with a global
counter domain when feature caching is
enabled
Feature order is important
If global count domains are not working
correctly, try the latest version of FME

35. Evie Lapalme, Clipper
Clipper

36. The Clipper
● This transformer cuts out an input
feature (candidate) to a defined
feature boundary (clipper).
● The inputs can be anything from
points, lines, polygons, rasters, or
even surfaces, solids, and point
clouds.
Candidate Clipper Inside Port Outside Port

37. Demo

38. Tips & Tricks
● What can you clip? Anything!
● Remember what version of the
Clipper you are using.
● Break down your data before input
to save on computer processing.
Resources
● Article Using the Clipper Transformer
https://community.safe.com/s/article/example-using-the
-clipper-transformer
● Webinar Clip, Clip, Hooray! Get Just the Data You Need
with Clipping
https://www.safe.com/webinars

39. Next Steps
Download
FME 2022.2 &
the webinar
workspaces
Apply the
workspaces to your
own data to
manage changes
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Contact us. We
would love to chat
with you about
anything data
info@safe.com

40. Full gallery: safe.com/transformers
● 500+ transformers
● “Help Me Choose a
Transformer”

41. Check out our upcoming
& on-demand webinars:
safe.com/webinars

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September 5-7
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44. Q&A

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