What does it take to transform FME Flow from a powerful ETL tool into a robust enterprise platform? This session dives into the critical architectural decisions that can make or break your FME Flow deployment, whether it’s your first implementation or a major scaling effort. We’ll explore the key choices for designing an FME Flow architecture that fits your IT landscape, integrates smoothly, ensures security, flexibility, and supports future growth. We'll also address practical questions like: should it be fault-tolerant? Is a single server enough, or do you need a distributed setup? What about infrastructure—cloud, on-premises, or hybrid? And for engines, should they be remote or distributed? These are the kinds of decisions that impact how well FME Flow will perform, how much it’ll cost, and how easy it’ll be to keep running smoothly. In the second half, we’ll bring theory into focus with real-world architecture examples, from smaller setups seeking cost-effective automation to mission-critical environments that require high availability and seamless integration with other enterprise tools. Whether you're new to FME or a seasoned expert, this session offers actionable insights to bridge strategic planning with practical outcomes.

1. Unlocking FME Flow’s
Potential:
Architecture Design for
Modern Enterprises
The Peak of Data
and AI 2025

2. 2025
The
Peak
of
Data
and
AI
Théo Drogo
GIS Strategic Advisor
Jean-Nicholas Lanoue
GIS Strategic Advisor

3. 2025
The
Peak
of
Data
and
AI
Unlock the full potential of
FME Flow by making the
right architectural decisions,
whether you're deploying it for
the first time or scaling it
across the enterprise.

4. 2025
The
Peak
of
Data
and
AI
In Scope (We will cover…)
●Architecture Fundamentals
●Express, Distributed or Fault-Tolerant?
●Focus on Engines
●Key architecture decision points
●Real-world architecture examples
Out of Scope (20 minutes only 😉)
● FME Flow Hosted
● Infrastructure As Code
● FME Flow Administration concepts
● Containerization
● Licensing models
● And everything you won’t see in the next slides 👀

5. 2025
The
Peak
of
Data
and
AI
Architecture Fundamentals
● 5 essential components
● Each play a specific role in execution &
orchestration
● Applies to all deployments
… Only 5, but they scale in many ways

6. 2025
The
Peak
of
Data
and
AI
3 main deployment options:
● Express
● Distributed
● Fault-Tolerant
Express, Distributed, Fault-Tolerant

7. 2025
The
Peak
of
Data
and
AI
Engine deployment options
● Same server, same network
● Different server, same network
● Different server, different network
Engine type in 1 word!
● Standard engine : classic.
● CPU-based engine: scalable.
● Remote Engine: nomad.
Focus on Engines

8. 2025
The
Peak
of
Data
and
AI
Cloud
On-prem
Database
Linux
Windows
Hardware
ArcGIS licensing
Web Server
Compatibility Performance
Other important considerations

9. 2025
The
Peak
of
Data
and
AI

10. Industry context
Small to mid-sized enterprise in the agri-environmental sector. GIS supports engineering and agronomy work but is not core to the business.
Business Technology
Usage Scenarios
5–10 FME users
Low volume of geo and tabular data
Not mission-critical
Infrastructure strategy
Fully on-premises environment
All apps deployed on internal VMs
Budget
Restricted budget
Cost-efficiency is a priority
Technology stack
Linux-only backend
QGIS, GeoServer, PostgreSQL
Scalability
No short-term scaling needs
FME is not the corporate ETL
Data location
Internal file servers and database
Public APIs
Open data portals
Compliance & Governance
Open ecosystem
No specific regulatory constraints
Availability No high availability or failover required
In-house Capabilities
2 GIS technicians, supported by IT
New to FME but tech-comfortable
Security
Deployed behind corporate firewall
SSL enforced for all services
Server have internet access (outbound)
Real-life scenario

11. ● Distributed engine (ArcGIS Server) + DB
on SQLServer (Corporative rules)
● Local governement
● Low Budget
● All Windows environment
● DB admin have expertise on SQL Server
Real-life scenario

12. Real-life scenario
● Fault tolerant – hybrid cloud/on prem
● 2 groups use FME Flow (IT + GIS)
● FME is the corporate ETL
● Large government or Utilities entities

13. Conclusion

14. Quizz

15. 2025
The
Peak
of
Data
and
AI
“Why use one engine when you can
deploy five… in three networks… and
regret it later.”
— ChatGPT 4o on a Tuesday PM

16. 2025
The
Peak
of
Data
and
AI
ThankYou