The Cloud Native Geospatial revolution accelerates: FME 2025 and the future of spatial data integration

Building on our previous exploration of cloud native formats and DuckDB integration, 2025 has brought revolutionary changes to how we work with geospatial data. From streaming capabilities to spatial computing, here's what's transforming the landscape.

The Storage Revolution Leap

Last year, we explored how FME 2024.1's DuckDB integration transformed our ability to query massive cloud native datasets without local storage. Fast forward to 2025 and the geospatial industry has reached what Chris Holmes described as 'a pivotal moment' at the inaugural Cloud Native Geospatial Conference. Chris went onto explain that we now have more data, better tools and greater computational power than ever before - the question has evolved from "how do we get there?" to "how do we harness these resources for real-world impact?"

The traditional, on-premise methods of managing data can be rigid, expensive and unable to keep pace with the sheer volume and change updates of data sources. Cloud Native Geospatial Data Formats have helped to shift away from on-premise data silos. This approach isn't to moving your existing databases to a cloud server. It's about re-architecting spatial data management to use the capabilities of the cloud -scalability, flexibility and powerful processing.

Building the Bridge: why the 'On-Ramp' matters

The Cloud Native Geospatial Conference 2025 dedicated an entire track to the 'On-Ramp to Cloud-Native Geo' recognising that the journey from traditional GIS workflows to cloud native approaches requires the right tools and approach.

With over 250 attendees from 100+ organisations, the conference highlighted a critical industry reality: 45% of participants were senior technical professionals already working with geospatial data, yet they needed structured support to transition to cloud native workflows. This isn't about learning entirely new concepts but rather understanding how familiar geospatial principles apply in cloud native environments.

There are of course a growing range of fantastic open source ETL tools, in this blog we will however focus on how FME has emerged as a valuable bridge technology for this transition, offering comprehensive support for cloud native formats whilst maintaining compatibility with existing GIS investments. Let's explore how this on-ramp actually works in practice.

The Cloud Native Format Landscape: What You Need to Know

Building on our previous exploration of cloud native formats and DuckDB integration, the ecosystem has matured significantly. The 'on-ramp' approach recognises that organisations don't need to understand every technical detail - they need to know which formats solve which problems.

The Core Cloud Native Formats FME Supports

Raster Data: Cloud Optimised GeoTIFF (COG)

• What it solves: Efficient streaming of large raster datasets
• FME capability: Read/write support since 2023.1
• Use case: Satellite imagery, aerial photography, elevation models
• Why it matters: HTTP Range Requests mean you only need to request the resolution and extent you need for your analysis

Vector Data: GeoParquet & FlatGeoBuf

• What they solve: Efficient querying of large vector datasets
• FME capability: Read/write support, DuckDB integration for advanced querying through the DuckDB Reader and SQLExecutor transformer
• Use case: Building footprints, transport networks, administrative boundaries
• Why it matters: Query millions of features without downloading entire datasets

Point Clouds: Cloud Optimised Point Cloud (COPC)

• What it solves: Streaming access to LiDAR data
• FME capability: Read/write support
• Use case: Infrastructure surveys, forestry analysis, urban planning

Multi-dimensional Arrays: Zarr

• What it solves: Climate data, weather models, time-series rasters
• FME capability: Read and Write support with expanding functionality
• Use case: Climate analysis, environmental monitoring
• Why it matters: Slice through time and space dimensions efficiently

Asset Cataloguing: SpatioTemporal Asset Catalog (STAC)

• What it solves: Discovering and organising geospatial assets
• FME capability: STAC Asset Reader and Metadata Reader
• Use case: Managing large collections of imagery or data products
• Why it matters: Find exactly the data you need across vast archives using a catalogue and API that is widely supported (FME, ESRI, QGIS)

The FME Advantage: Making Complex Simple

What makes FME particularly valuable for the cloud native transition is its approach to complexity management. Rather than requiring teams to become experts in cloud storage APIs, HTTP range requests, or columnar data structures, FME provides familiar interfaces that handle the technical complexity behind the scenes, all within FME's no-code environment.

Conclusion - the 'On-Ramp' is open

The Cloud Native Geospatial Conference 2025's focus on the 'on-ramp' reflects a maturing evolution on the Cloud Native Formats stacks. The technologies, standards, and with the FME Platform - the tools enable cloud native geospatial to be accessible to mainstream GIS professionals.

FME's comprehensive support for cloud native formats, combined with its familiar interface and robust processing capabilities, provides the most practical on-ramp available today. The organisations that succeed in this transition will be those that recognise cloud native geospatial not as a destination but as a foundation for even more capabilities and functionality ahead.

CNG Shout Out

Why not consider joining the Cloud Native Geospatial (CNG) Community cloudnativegeo.org/join/ connecting with a community of data practitioners with access to a members-only Slack channel and they opportunity to be part of projects that are transforming the geospatial sector.

Would you like to know more?

Ready to learn more about cloud native geospatial data formats and look to transform your data strategy? Contact us to discover how our expertise can help you build an efficient, future-proof data foundation.