The document discusses dynamic workspaces in data management, emphasizing their adaptability to varying data types and structures without predefined schemas. It highlights use cases, components like the schema scanner transformer, and the benefits of automation in workflows. Additionally, resources and next steps for engaging with FME training and community are mentioned.

1. Dynamic Workspaces
Demystified: Your Path to
Streamlined Data
Management

2. Dan
Minney
Customer Solutions Specialist
Chris
Berger
Customer Solutions Team Lead
Meet the Team

3. Welcome to Livestorm.
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5. Agenda
1 Introduction & Components of a Dynamic Workspace
2 Use cases & demos
3 The SchemaScanner Transformer
4 Advanced Dynamic Workflow
5 Conclusion
7 Resources and Q&A

6. Poll:
How many hours do you
use FME weekly?

7. 1
Introduction

8. One platform, two technologies
FME Form FME Flow
Build and run data workflows Automate data workflows
FME Flow Hosted
Safe Software managed instance
fme.safe.com/platform
FME Enterprise Integration Platform

9. Number
of
supported
data
types
in
FME
1995 2000 2005 2010 2015 2020 2023…
10
100
300
500
GIS
CAD
Database
XML
Raster
3D
BIM
Web
Point
Cloud
Cloud
Big
Data
IOT
Gaming
BI
Indoor
Mapping
AR/VR
Generative
AI
Cloud
Native
Tabular
Unrivalled Data Support

10. What is a Dynamic Workspace?
A Workspace that integrates data without predefined source and/or destination
schemas.
Adaptive & Versatile
● Schema is determined at runtime, adapting to the data on the fly.
● Easily adjusts to different data structures and formats as they are encountered.

11. Why use a Dynamic Workspace?
Schema independent workspaces are beneficial because they can handle varying
inputs, whether that be changing data types, attributes, formats or feature types
● Minimal long-term maintenance
● Can handle varying output formats
● Better suited for un-predictable source datasets
When to use one?
● Best used for simple translations between formats
● Helpful if the structure of the source data might change

12. Fixed Schemas
Traditional workspaces are tightly bound to source and destination schemas.

13. Dynamic Schemas
Dynamic workspaces break up the dependence on a set schema by creating a
universal layout to handle the data

14. Source: In the Reader, use the Merge Feature Type parameter to enable dynamic
inputs
Components of a Dynamic Workspace

15. Destination: In the Writer, enable Dynamic Schema Definition
Components of a Dynamic Workspace

16. On the Start Tab, select Generate Workspace and choose Dynamic Schema
Easy Place to Start

17. Automating Dynamic
Workspaces

18. Event Driven Real Time
FME Flow Apps
- Submit Jobs and Trigger Automations
- Stream or Download Job Results
FME Flow Automations
- Polling Services
- Email Received, FTP/S3/Directory Modified, etc.
FME Flow REST API
- Job as a Webhook
FME Flow Automations
- Push Services
- Webhook/Message Received, Topic Notified,
etc.
FME Flow: Automating Dynamic Workspaces

19. Form & Flow: Dynamic Workspaces Concepts
FME Flow Automations:
Run a Dynamic Workspace

20. FME Form Authoring:
Dynamic Workspaces
● Data-driven data integration that is
adaptable to various input types,
schemas, geometries, and outputs.
● Authored in FME Form and Published to
FME Flow
Form & Flow: Dynamic Workspaces Concepts
FME Flow Automations:
Run a Dynamic Workspace
● Enables organizations to specify the
workspace that is to be executed at
run-time rather than at “author time”.
● Users can invoke any workspace that
matches a template workspace without
having to stop the FME Flow Automation.

21. Poll:
Would you find value in and
attend a webinar on building
Dynamic Automations?

22. FME Form Authoring:
Dynamic Workspaces
● Flexible Data Handling: Adjusts to
different data sources and formats.
● Data/Parameter Driven: Use of
parameters and conditional logic to
control the flow of data.
● Reusable: Easily repurposed for different
tasks with minimal changes.
Form & Flow: Dynamic Workspaces Concepts
FME Flow Automations:
Run a Dynamic Workspace
● Automation: Executes specific
workspace based on predefined
conditions, reducing manual intervention.
● Continuous Integration: Enables
continuous data processing and
event-driven workflows.
● Scalable: Built for large-scale operations
and complex workflows.

23. 2
Use Cases &
Demos

24. Demo

25. Destination Schema as Mirror Image of
Source Data
● Maximum flexibility, simple setup
● Accepts any dataset in chosen format
○ Ex: Convert File Geodatabase table to CityGML, preserving schema
● Source dataset changeable, translation remains effective
● No workspace changes needed; select source dataset and run

26. Destination Schema is Derived from an
External Dataset
● Workspace reads data, copies schema to destination writer
● Output mirrors input dataset
● Map source data to new schema dynamically
● Useful for strict or pre-existing output schemas
● Use any format as template with a Resource Reader

27. Chat Storm:
What are you currently using your
dynamic workspaces for?

28. 3
SchemaScanner
Transformer

29. SchemaScanner Transformer
● Allows you to easily extract and manipulate the schema of
your datasets, tackling dynamic workspace issues such
as schema standardization and schema drift
● Remove attributes from the schema without having to
expose it
The schema feature is given the special attribute and value:
fme_schema_handling = ‘schema_only’
* FME 2021.2 or newer
required

30. Why use the SchemaScanner?
● You want to ensure the dynamic writer is
receiving a valid schema
● You don’t know the schema of the incoming
data and want to ensure it meets certain
standards before reaching the dynamic writer
● You want to modify the schema and need to
re-scan it before reaching the dynamic writer
* FME 2021.2 or newer
required

31. Key Parameters in the SchemaScanner
Output Schema Features Before Data
● A Dynamic Writer must receive the
schema first before the incoming data,
otherwise no data will be written out
● Suggested to set this to Yes, regardless of
your workflow

32. Key Parameters in the SchemaScanner
Ignore Attributes Containing
● Most schemas (once in FME) contain
format attributes you don’t need in your
final output dataset
● Example: csv_|multi_|fme_ would remove
attributes such as csv_line_number,
csv_type, fme_feature_type,
multi_reader_id, etc.

33. SchemaScanner Output
● A list attribute is generated that contains the
attribute name and its data type. This can then
be exposed or manipulated to create flexible
schema workflows.

34. FeatureReader vs SchemaScanner
● The Schema generated by the
FeatureReader is a copy of the dataset
schema
● The Schema generated by the
SchemaScanner is FME’s “best guess”

35. Slide Title
Fanout data
based on the
“Day” attribute
Goal Block Key
Dynamic Dataset Fanout
Result
Multiple datasets
+ schema has
changed since
data was read
into FME
Group By
Parameter in the
SchemaScanner
Output a CSV file
for each day of
the week, even
after altering the
schema

36. Demo

37. 4
Advanced
Dynamic
Workflow

38. ● Identify and respond to changes in the schema of incoming data
● Scenario
○ If there is a mismatch between the existing schema and the data
submitted by the user, then the user will be alerted of the schema
mismatch and be required to resolve it accordingly
● FME can automatically identify, flag, and address the schema
mismatch
● Eliminates downstream processes from being disrupted
Schema Validation App

39. FME Flow Workspace Apps
User uploads file
to FME Flow
Workspace App

40. Invalid Schema
HTML Report
Data upload
confirmation
FME Flow Workspace Apps

41. Schema Drift Detection

42. Test Validation
● VariableSetter & VariableRetriever
direct flow of data based on Schema
Validation results
● Tester will pass or fail features based
on the value set for the
“invalid_schema” variable

43. Schema Report Generation

44. Integrate Valid Features
● FeatureMerger merges the user
submitted data back in before
writing to the SpatiaLite table
● AttributeRemover cleans up
attributes created from the
VariableSetter/VariableRetriever

45. Demo

46. Poll:
Have you used the
SchemaScanner?

47. 5
Conclusion

48. Summary
● Dynamic workspaces handle changing
inputs, feature types & attributes
● Various dynamic workflows serve
different purposes
● SchemaScanner enhances dynamic
workflow capabilities in FME
● Advanced dynamic workflows aren’t
limited to just reading/writing data

49. 6
Safe
Software

50. Safe Software is recognized as Customers’
Choice again in the 2024 Gartner Peer
Insights ‘Voice of the Customer’: Data
Integration Tools report.
We are now recognized as Customers’
Choice in North America and Midsize
Enterprise segments.
GARTNER is a registered trademark and service mark, and PEER INSIGHTS is a registered trademark, of Gartner, Inc. and/or its affiliates in
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purpose.
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51. 29+
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128
190
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years of solving data
challenges
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members
countries with
FME customers
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30+
29K+
128
140+
25K+
years of solving data
challenges
FME Community
members
countries with
FME customers
organizations worldwide
global partners with
FME services
200K+
users worldwide
Safe & FME

52. 7
Resources

53. Resources
● Dynamic Workflows Article
○ Tutorial Series
● SchemaScanner Article

54. Get our Ebook
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Resources

56. 8
Next Steps

57. Peak of Data Integration 2025
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60. 9
Q&A

61. ThankYou
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