Imagine building web applications or dashboards on top of all your systems. With FME’s new Data Virtualization feature, you can deliver the full CRUD (create, read, update, and delete) capabilities on top of all your data that exploit the full power of FME’s all data, any AI capabilities. Data Virtualization enables you to build OpenAPI compliant API endpoints using FME Form’s no-code development platform. In this webinar, you’ll see how easy it is to turn complex data into real-time, usable REST API based services. We’ll walk through a real example of building a map-based app using FME’s Data Virtualization, and show you how to get started in your own environment – no dev team required. What you’ll take away: -How to build live applications and dashboards with federated data -Ways to control what’s exposed: filter, transform, and secure responses -How to scale access with caching, asynchronous web call support, with API endpoint level security. -Where this fits in your stack: from web apps, to AI, to automation Whether you’re building internal tools, public portals, or powering automation – this webinar is your starting point to real-time data delivery.

1. This is Where We Will
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2. Sanae
Mendoza
Customer Solutions
Specialist
Meet the Presenters
Don
Murray
CEO

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5. Agenda
1 Introduction to Data Virtualization
2 Data Virtualization 101
3 Create a Data Virtualization API
4 Create a Data Virtualization Endpoint
5 Authoring an Endpoint Workflow
6 Building Better Endpoints
7 Enriching Endpoints
8 Conclusion
9 Resources & Next Steps
10 Q&A
Agenda

6. 1
Introduction

7. FME’s Data virtualization empowers
your enterprise to deliver real-time,
code-free access to trusted data
across systems.
No duplication, no delays.

8. What is Data Virtualization?
● A method to access and query data
in real-time, without moving or
copying it.
● Think: unified views across multiple
sources, instantly.

9. Common Frustrations Organizations Face
Without Data Virtualization
● Siloed systems and inconsistent data
● Time-consuming data movement and ETL
● Redundant storage costs
● Delayed insights due to refresh cycles
● Complex integrations with multiple tools
Do any of these feel familiar?

10. You don’t need to wrangle data
manually or wait on an IT team.
With FME’s Data Virtualization,
you can connect, transform, and
access what you need:
on demand.

11. Simplifying Data Access
Live Data
Access
Simplified
Data Views
Flexible
Connections
Grow with
your needs
Deliver real-time
data through
secure APIs.
Make complex
systems easy to
connect to and
use.
Link systems
without heavy
upgrades or
rework.
Add new data
sources and users
without slowing
down.

12. The only All-Data, Any-AI Platform.
FME Form FME Flow
All Data workflows are built here.
Brings life to FME Form workflows
FME Flow Hosted
SaaS version of FME Flow
fme.safe.com/platform
FME Enterprise Integration Platform
Safe & FME
FME Realize
Experience your data where it
matters most. In context

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
Gaming
BI
Indoor
Mapping
AR/VR
Generative
AI
Cloud
Native
Tabular

14. 2
Data
Virtualization
101

15. Breaking Down Data Virtualization in FME
🧠 In Theory
● Data virtualization means managing
access, not making copies
● No data movement
● Just a layer that connects to what already
exists
🛠 In Practice (with FME)
● FME builds REST APIs from your live
data
● That layer becomes an API
● Other tools can request exactly what they
need—live, filtered, structured

16. Breaking Down Data Virtualization in FME
💡 Virtualized at the backend, accessed via REST APIs, delivered
through FME — no custom code required.

18. How does it compare to Webhook URLs and FME Flow REST API?
Webhook URLs
● Basic event triggers
● GET/POST only
● Limited input/output
● No schema or docs
● Not a full API
● No customization
FME Flow REST API
● FME Flow
management focused
● Limited integration
● Limited input/output
● No customization
Data Virtualization APIs
● Integration focused
● Full data access
● Full CRUD
● Full customization
● Docs + schema support
● Secure, user-friendly

19. Where Data Virtualization Starts… and Stops
❌ Not Read-Only
Supports full CRUD operations.
❌ Not Just for Enterprises
Scales from small teams to large organizations.
❌ Not an API Gateway
Provides real-time data access, not traffic control or authentication.
❌ Not a Data Warehouse Replacement
Delivers live views without storing or duplicating data.

20. Scenario 2: Legacy Systems Blocking Integration
Problem: Critical data lives in old systems that are hard to connect to,
slowing down projects and raising costs.
Solution: Use FME Data Virtualization to access legacy data
directly—no migration needed.
Value: Fast, low-cost integration that keeps systems running without
disruption.
What can I do with FME Data Virtualization?

21. Scenario 3: AI Tools Need Better Data Access
Problem: Teams want to use AI tools, but connecting them to existing
data workflows is slow and expensive.
Solution: Expose FME workflows as virtual endpoints that AI can
query in real time.
Value: Smarter decisions, faster answers, and AI that actually fits your
data context.
What can I do with FME Data Virtualization?

22. Scenario 1: Department Reports to Integrated Dashboards
Problem: Teams are emailing Excel reports weekly. Data is copied
manually into dashboards, leading to delays and mistakes.
Solution: Use Data Virtualization to expose those Excel files as live,
queryable data sources—no more manual steps.
Value: Updates are instant, errors drop, and managers get accurate
dashboards without waiting.
What can I do with FME Data Virtualization?

23. Flash
Demo

25. 1. Real-time dashboard with region + capacity filters
2. Live API calls backed by Data Virtualization
3. Fully documented in Swagger
4. Powered by a simple FME workspace (Excel + GeoPackage)
Demo: Emergency Facilities Dashboard

26. 3
Creating a Data
Virtualization API

27. Data Virtualization in
FME Flow
● API Configuration
○ API Details
○ Schemas
○ Endpoint Metadata
○ Security
○ Workspace Generation
Right click and select ‘replace image’ to add an example image from your project.

28. Data Virtualization in
FME Form
● Endpoint behaviour
○ Data sources
○ Transformations
○ Analysis
○ Format API response
Right click and select ‘replace image’ to add an example image from your project.

29. FME Flow
API Configuration
Schemas
Endpoint Metadata
Security
Workspace Generation
FME Workbench
Endpoint behaviour
Data sources
Transformations
Analysis
Generate API response
Endpoint Authoring

30. OpenAPI Specification (OAS)
A standardized way to describe REST APIs in a machine-readable format (JSON)
Defines endpoints, methods, request/response formats, and authentication
Import and Export APIS to FME Flow
Swagger UI
API Documentation and interactive
testing.

31. Testing with API Clients
● OpenAPICaller
● HTTPCaller
● Swagger Documentation (Web)
● Browser
● Postman

32. From Import…
● Import an OpenAPI (OAS) file (JSON)
● Auto-generates structure and methods
● Less manual setup
● Useful for migrating existing APIs
From Scratch…
● Manually define API structure
● Full control over endpoints and
formats
● Ideal for custom APIs
● No existing documentation
required
Creating a Data Virtualization API

33. API Security
● Secure by endpoint or API-wide
● Role- or user-based access
● Managed separately from FME Flow
permissions
● Works with external systems
● Supports Basic Auth and API Tokens

34. Demo

35. Demo: Creating a Data Virtualization API

37. 1. Imported OpenAPI Specification file (EnvironData.json) to create an API
2. Toured the Data VIrtualization Interface in FME Flow
3. Exported the EnvironData API OpenAPI file to use in the FME
Workbench and Postman
Demo: Creating a Data Virtualization API

38. 4
Creating a Data
Virtualization
Endpoint

39. What is a Data Virtualization API Endpoint?
● Defines how users access your data
● Customize requests, responses, parameters, status codes, headers, schemas, security, and
metadata
● Supports both static and dynamic responses
● Defined in FME Flow, authored in FME Workbench

40. Defining Endpoint Metadata in Flow

41. Defining Endpoint Metadata in Flow

42. Manual vs Workspace Responses
Manual Workspace
Response Always the same Changes based on inputs or data state
Backend No workspace Workspace powers response
Data source None (static) Connected to live data
Use Case Metadata, service status Queries, analysis, live data

43. Workspace Responses
● Powered by an FME workspace
● Response changes based on inputs and live data
● Supports querying, transformation, and analysis
● Ideal for dynamic, real-time APIs
● Flexible functionality: parameters, richer methods, multiple status codes, etc.

44. Inside a Workspace Response

45. 5
Authoring an
Endpoint
Workflow

46. Generate, Author, Publish

47. Generate, Author, Publish

48. Defining Endpoint Metadata in FME Flow

49. Defining Endpoint Metadata in FME Flow

50. Schemas
● Define the structure of requests and responses
● Improve API usability, validation, and reliability
● Help developers understand what to send and expect
Two main types:
○ Request Body Schemas (for POST requests)
○ Response Body Schemas (linked to status codes)

51. FME Flow
Schemas
everywhere!

52. API Docs
FME Flow
Schemas
everywhere!

53. API Docs
Workspace
FME Flow
Schemas
everywhere!

54. Workspace Generation and Management
Right click and select ‘replace image’ to add an example image from your project.

55. Generate, Author, Publish

56. Workspace Generation and Management
Right click and select ‘replace image’ to add an example image from your project.

57. Reader
Represents the API Request
● Creates a feature type for every endpoint
● Attributes: Endpoint path, parameters, body
Writer
Represents the API Response
● Always a single feature type
● Attributes: Response status code, headers, and body

58. Building Blocks of a Data
Virtualization Workspace

59. Better, Faster, Stronger APIs
Purpose Tools
Connecting to external data sources and
joining datasets
FeatureReader, FeatureWriter, DatabaseJoiner, InlineQuerier,
SQLCreator, SQLExecutor, HTTPCaller, AttributeValueMapper
Formatting response bodies and parsing
request bodies
JSONTemplater, JSONFragmenter, JSONFlattener
Creating status codes and headers AttributeCreator, AttributeManager
Validating and filtering request
parameters
Tester, TestFilter, AttributeFilter, AttributeValidator
Fast authoring Save attributes as parameter presets

60. Templates
Input templates simulate API requests
● Simulate API requests with sample data
● Include method, path, headers, body, and
parameters
● Save requests to reuse for testing
● Use in FME Form to test logic, validate
responses, and debug locally
Output templates represent API responses
● Defined schema, data types, status codes,
content types, headers, etc.

61. Demo

62. Demo: Creating an Endpoint with a Workspace Response

64. 1. Created the GET /facilities endpoint
2. Generated an endpoint workspace
3. Authored an endpoint workspace
a. Combined data sources: Excel & GIS
b. Formatted a JSON response
c. Created status codes
4. Republished to FME Flow
Demo: Creating an Endpoint with a Workspace Response

65. 6
Building
Better
Endpoints

66. Best Practices
● Request parameters
● Routing for status codes
● Error catching
● Built-in data validation + cleaning

67. Request Parameters
Endpoint parameters allow users to refine requests and customize responses, improving the
flexibility, efficiency, and overall usability of the API.
Type Use Case Example
Path Identify a specific resource /users/{userId}
Query Filter, sort, or control response content /wildfires?region=west&year=2023
Headers Provide metadata or authentication
Authorization: Bearer {token}

68. Designing Workspaces for Parameters
Design workspaces to check for parameter input and apply filters conditionally for better
performance and flexibility.
Type Tools Pros & Cons
Reader Parameters WHERE Clauses, SELECT, Spatial
Filters
Very efficient - filter before loading
Querying Transformers InlineQuerier, SQLExecutor,
DatabaseJoiner
Very efficient - filter & join before loading
Filter Transformers Tester, Sampler Less efficient - filter after loading

69. Workflow: Updating Endpoint Metadata

70. Workflow: Updating Endpoint Metadata

71. Demo

72. Demo: Adding Parameters to an Endpoint

73. Demo: Adding Parameters to an Endpoint

74. Demo

75. 1. In FME Flow, updated GET /facilities endpoint to have path and query
parameters (path.region, query.min_capacity)
2. In FME Form, updated the facilities.fmw workflow to filter the Excel and
Geopackage data.
3. Republished to FME Flow
Demo: Adding Parameters to an Endpoint

77. 7
Enriching
Endpoints

78. Files and Resources
● Accept file uploads (images,
documents, datasets)
● Use multipart/form-data in
requests
● Return files like PDFs, reports, or
shapefiles
● Set content type and filename with
response headers

79. Caching
● Faster Responses: Store common
results to avoid repeated processing
● Reduced Load: Less strain on backend
systems and databases
● Improved Scalability: Serve more
users with fewer resources
Limitations: Cache stored for path
parameters, not query, headers, or bodies.

80. Job Expiry
● Prevents Stale Requests: Stops old or
abandoned jobs from consuming
resources
● Handles Timeouts Gracefully: Ensures
long-running jobs don’t block the system
● Improves System Stability:
Automatically cleans up jobs stuck in
queues or processing

81. Asynchronous Processing
● Handles Long-Running Tasks:
Processes jobs without blocking the
client
● Improves User Experience: Clients
can check back later or get notified
when complete
● Boosts API Resilience: Prevents
timeouts and reduces strain on Flow

82. Asynchronous Processing
{
"requestID": {request-id},
"statusUrl": "/api/status/{request-id}",
"resultUrl": ”/api/result/{request-id}"
}
GET </api/EnvironData/facilities/all>
GET </api/status/{request-id}
GET </api/result/{request-id}
{
"requestID": {request-id},
"status": "success",
"statusMessage": "Translation Successful"
}
[
{
"city": "Williams Lake",
"facility name": "Cariboo Community Hall".....
API Request
Check Status
Get Results

83. When things go wrong: Debugging Tips
Custom vs. System Status Codes
● 500: Job failure or other server side
● 401: Authentication issue
● 404: path not found
● 403: Unauthorized
● 415: Unsupported media type - Flow only
supports application/json, multipart/mixed,
and multipart/form-data.
Job Log files
● Check request parameters
System Log Files
● Need permissions

84. 8
Conclusion

85. Summary
Data virtualization delivers a
real-time, unified view of your
data.

86. 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

87. 8
Resources

88. Data Virtualization
Resources
Learn
● Tutorial: Getting Started with
Data Virtualization
Action
● Download the newest release
2025.1 Beta!
● Share your workflows with us!

89. Get our Ebook
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90. 9
Next Steps

91. We’d love to help you get
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93. 10
Q&A

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