Data Virtualization in Action: Scaling APIs and Apps with FME

Imagine building secure, scalable apps without writing a single line of code. This advanced session builds on our first webinar, showing you how to deploy scalable, secure APIs that power real-time apps and dashboards using FME.

In this advanced session, you’ll learn how to scale and secure your APIs for real-world production using reusable templates and smart configuration techniques.

Join us and guest speakers from con terra, as we explore their OGC API templates and dive into how FME can be used as the backend to power a multi-faceted web app. Plus, get a look at some advanced features and what’s coming next: complex schema support, asynchronous execution, and callback URLs.

You’ll learn how to:

-Expand FME Data Virtualization projects with reusable API templates to quickly expose standardized, scalable interfaces for internal and external data access
-Secure and govern real-time data services with fine-tuned controls
-Scale performance using async workspace execution and callback URLs

Whether you’re looking to level up internal tools or build high-performing public apps, this webinar will help you transform FME into a robust API delivery engine, and move from concept to deployment with confidence.

1. Data Virtualization in
Action: Scaling APIs and
Apps with FME

2. Dennis
Wilhelm

Software Architect
con terra
Rylan
Maschak

Group Product Manager
Safe Software
Nick
Ison

Product Manager, Cloud Team
Safe Software
Meet the Presenters

3. Welcome to Livestorm.
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5. Agenda
1Introduction
2Reusable API Templates
3API Authentication and Authorization
4What’s New & Coming
5Conclusion
6Resources & Next Steps
7Q&A

Agenda

6. 1
Introduction

7. Deliver real-time, secure, and
scalable APIs with FME: no
code required.

8. Flash
Demo

9. ●Unified access to data across apps,
tools, and platforms
●On-demand data delivery for
dashboards and web apps
●Centralized control over data exposure
and access
●Rapid iteration and deployment
without coding
●Seamless integration with Esri and
enterprise systems


●

With Data Virtualization

10. Challenges You Might Face
Common frustrations people face with this topic are:
●Starting from scratch: no templates or examples
●Difficulty securing APIs while enabling real-time access
●Performance bottlenecks with high usage
●Complexity in handling multiple schema types or data formats
●Lack of visibility and control across departments

11. Solve these challenges by combining
reusable templates, built-in governance
tools, and performance-enhancing features
like caching, async execution, and schema
flexibility – making scalable API deployment
achievable without coding.

12. The only All-Data, Any-AI Platform.
FME Form FME Flow
Data Movement and transformations
(“ETL”) workflows are built here.
Brings life to FME Form workflows
FME Flow Hosted
Safe Software managed FME Flow
fme.safe.com/platform
FME Enterprise Integration Platform
Safe & FME
FME Realize
Experience data in real world
context, in real time.

13. With 500+ supported data types in FME.
Unrivalled Data Support





GIS
CAD
Database
XML
Raster
3D
BIM
Web
Point
Cloud
Cloud
Big Data
IOT
Graph
BI
Indoor
Mapping
AR/VR
Generative
AI
Cloud
Native
Tabular

14. 2
Reusable API
Templates

15. ●Data Virtualization allows FME Flow to provide almost any API
●Why not use existing standards to integrate FME even better
in IT infrastructures!
Endless possibilities

16. New OGC APIs
New OGC API Legacy Usage
OGC API Features Web Feature Service Access to vector data
OGC API Maps Web Map Service Map visualization
OGC API Processes Web Processing Service Execution of processes & job
management
OGC API Coverages Web Coverage Service Raster data access
OGC API Records Catalogue Service for the WebMetadata queries

17. ●Simplified HTTP access to geospatial
●Easier integration with modern web clients and services
●Standardization reduces vendor lock-in
●Widely supported across the geospatial ecosystem

Why OGC APIs Matter

18. ●OGC API - Features
○Expose live vector datasets via OGC-compliant
endpoints
●OGC API - Processes
○Trigger FME Workspaces via a standardized API


●Developed by con terra, available on FME Hub
○Example implementation
○Can be customized for other use cases

Getting started using Hub Templates

19. “[…] supports the wrapping of computational tasks into
executable processes that can be offered by a server through a
Web API and be invoked by a client application […].
Typically, these processes execute well-defined algorithms that
ingest vector and/or coverage data to produce new datasets.”

https://ogcapi.ogc.org/processes/


OGC API Processes

20. OGC API Processes ↔ FME Flow

21. ●Request by Deutsche Telekom to provide an OGC API
Processes interface for FME
●Implementation of a Python based adapter
●Goal is to switch to native FME functionality instead of a
custom implementation
Initial motivation

22. „[…] offers the capability to create, modify, and query spatial
data on the Web and specifies requirements and
recommendations for APIs that want to follow a standard way of
sharing feature data.”

https://ogcapi.ogc.org/features/

OGC API Features

23. ●Access to vector data via
FME Flow
○Filter based on bbox,
datetime, properties, …
●FME implementation built for
API access, not to replace
map clients

OGC API Features

24. ●Data can now be consumed by any
software that has implemented the
specification
●Including of course FME

OGC API Features in FME

25. Demo

26. ●Define each API endpoint
●Create Workspaces
Setup: OGC API Processes

27. ●Read parameters from API input

Execute Processes Endpoint

28. ●Store job in the database
●Perform the actual workspace
execution
Execute Processes Endpoint

29. ●Provide response back to the
client

Execute Processes Endpoint

30. Main Processing Steps
●Mapping between parameters from FME and OGC API Processes
●Trigger jobs using the different FME services
●Use a custom job database to
○Map between UUID and FME job id
○Provide async datastreaming
●Provide required metadata
○Descriptions
○Links to resources
⇒ Everything is already done in the templates for you :-)

31. Demo

33. “Writing your own API without
standards is like cooking without a
recipe—sure, it might work, but good
luck explaining it to anyone else.”

— ChatGPT

34. Wrap-up
●Providing your data or processes via a standard will
improve interoperability and user experience
●Templates provide an easy way to get started without
reading through the full API specs first

35. Thank you
Dennis Wilhelm
Software Architect
con terra
[email protected]

36. 3
API
Authentication
and Authorization

37. ●The Next Leap – Pushing Boundaries in Data & AI Innovation
○Starting at 53:30 - introduction to the project and a good high-level
overview of a demonstration project and how it all works

●Been There, Mapped That: API (& AI) Adventures in Data Virtualization
○Deeper dive into the technical components of how Data Virtualization was
used to build a backend API, and building a front-end to interact with it



Building on The Peak of Data and AI 2025:

40. Authentication &
Authorization in
Data Virtualization

41. Authentication Authorization
“Who are you?”
Verifies the identity of the user or
system (e.g., via username +
password, API key, OAuth token).

“What are you allowed to do?”
Determines what actions or resources
that authenticated user/system is
permitted to access.

42. Authentication proves identity;

Authorization controls access.

43. ●The ability to decide if a specific
endpoint is allowed to be queried
without credentials or with
credentials
●Uses FME Flow User Model

Authentication in Data Virtualization

44. Slide Title
No
Auth
FME
Flow
Auth
Custom
Auth
Three ways of handling Authentication

45. Slide Title
No
Auth
●Authentication is not used
●Anybody with the API URL can make a request
and get results
●Need to ensure that no sensitive data is available
●Any API being built for corporate purposes really
should have authentication setup as default

46. ●Authentication is checked when making a
request
●Uses FME Flow users and user groups
●Anybody with the API URL can make a request
and get results, but needs to have valid
credentials
●Can help gate keep sensitive information to those
who have credentials
●Easiest method of securing your endpoints
Slide Title
No Auth
FME
Flow
Auth

47. ●Granular control over who is allowed
to interact with your endpoints - by
role and/or user
Authentication in Data Virtualization

48. ●FME Flow user account that can create other
user accounts - eg: website signup process
●Easy:
○When user logs in, can use those credentials
as part of the header of the subsequent
requests
●Better:
○Use the parent account to generate an API
token for end user, save that in session and
use for subsequent requests
Slide Title
No Auth
FME
Flow
Auth

49. Authentication in Data Virtualization
Authentication
Type
Placement Configuration
Basic Header Authorization: Basic <base64-encoded " username:password">
Token Header Authorization: fmetoken token=< API-token>
Token Query Parameterfmetoken=<API-token>
More Details on FME Support Center

50. Slide Title
No Auth
Custom
Auth
●Authentication is checked as part of the FME
Workflow triggered by API Endpoint
○Technically, requests are initially performed
with ‘No Authentication’
●Can be used to check your own Authentication
source (Internal Database, Active Directory, etc.)
●Need to handle messaging/errors when user is
not allowed to access endpoint
○Eg. return HTTP 401 / Unauthorized instead
of HTTP 200 / OK

51. Example Custom Authentication

52. Example Custom Authentication

53. Example Custom Authentication

54. Authentication Authorization
“Who are you?”
Verifies the identity of the user or
system (e.g., via username +
password, API key, OAuth token).

“What are you allowed to do?”
Determines what actions or resources
that authenticated user/system is
permitted to access.

55. Authorization
●Authorization has different facets for every company, project, workflow
●This is where your business logic comes into play
●Can be managed with FME Flow user roles
●If you need more granular control (field level, row level, etc. ), you’ll need
to look at incorporating that into your individual endpoints/workflows

56. 4
What’s New
and Coming

57. Query String Caching
●Caches are now generated for
unique combinations of Query String
Parameters
●Follows the same caching
configuration (per Endpoint or API)


Available now in FME Flow 2025.1!

58. Complex Schema
Support
●Nested JSON Object Properties
●JSON Arrays
●Use Schemas for Properties
●Schemas in Schemas

●Supported when creating, editing, or
importing an OpenAPI specification

Coming soon in FME Flow 2025.2!

59. Flexible Content Type
Handling
●Expanded List of Predefined MIME
Types
●Manually Add Any Type
●Supports Better API Documentation


Coming soon in FME Flow 2025.2!

60. Long Polling for Async Endpoints
●Using query string parameters longPoll and longPollInterval to reduce
excessive polling and network traffic
●Available for all workspace endpoints with ‘Asynchronous Processing’ enabled

Coming soon in FME Flow 2025.2!

61. Long Polling for Async
Endpoints
●Using query string parameters
longPoll and longPollInterval to
reduce excessive polling and
network traffic
●Available for all workspace endpoints
with ‘Asynchronous Processing’
enabled

Coming soon in FME Flow 2025.2!

62. Support for
Callback URLs
●Configure an API Response to be
sent to another Data Virtualization
Endpoint
●Support for selecting an Automation
or any URL is on the roadmap

Coming soon in FME Flow 2025.2!

63. 5
Conclusion

64. Summary
●Use existing templates to jumpstart
your first API
●Solve real problems with FME’s Data
Virtualization—securely and at scale
●Define and test your endpoints early
before layering in advanced config
●Keep sharing your feedback—we’re
building this with you!

65. 30+
30K+
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
200K+
users worldwide

66. All Data. Any AI.
All Data Velocities
Batch (ETL, Reverse ETL, ...)
Event ( BPA, RPA, ...)
Stream
All Data Locations
Any Cloud
On-premises
Hybrid
Edge
Containers
Embedded
Mixed
All Data Types
Unstructured
Structured
Spatial
APIs
Web Apps
…
Any AI
Technology
OpenAI
Amazon Bedrock
Google Gemini
Ollama
Deepseek
Composite
...

67. 6
Resources

68. Data Virtualization
Resources
●OGC API Processes Template
●OGC API Features Template
●Getting Started with Data
Virtualization

69. Get our Ebook
Spatial Data for the
Enterprise

fme.ly/gzc


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70. 7
Next Steps

71. We’d love to help you get
started.
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73. 8
Q&A

74. Thank You
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