Every once in a while, a conversation topic starts surfacing repeatedly, unprompted, from completely different directions. This week, for me, that topic was cloud-native geospatial data.

Seven conversations in 24 hours. All roads leading to the same challenge: geospatial data is abundant but remains frustratingly difficult to use.

What struck me wasn’t just the volume of these conversations, but their near-identical patterns. Whether I was talking to a hydrologist wrangling snow data or a startup founder trying to streamline delivery routes, the sentiment was the same: “We have the data, but we’re drowning in the complexity of making it usable.”

The Hidden Cost of Traditional Data Delivery

Take SNODAS, for example, a critical snow model dataset that informs flood forecasting, reservoir planning, and even recreation. It’s powerful, but it’s still delivered in .dat files inside .tar archives on an old-school HTTP site. To make it usable? You need GDAL commands and a deep understanding of coordinate reference systems and no-data values.

This isn’t a technical hurdle, it’s a distribution and usability failure.

Cloud-Native Isn’t Just Cloud Storage

The common misconception is that putting data in the cloud is enough. But cloud storage just moves the problem, it doesn’t solve it. A 10GB TIFF on AWS S3 still needs to be downloaded in full before any insight can be gleaned. That’s like mailing someone an encyclopedia when they only needed a paragraph.

Cloud-native formats like Cloud-Optimized GeoTIFF (COG) and GeoParquet flip the script. They let the user filter and query data on demand, pulling only the bytes they need. Whether you’re on a cloud platform, a server, or a laptop, this format efficiency travels with you.

Interoperability Is the Key

The really exciting part? These formats open the door for a much wider set of users. Engineers and analysts working in an increasingly long list of tools can now access geospatial data with the same ease as structured tabular data. It’s breaking down the silos between spatial and non-spatial data systems.

That’s the real revolution here: spatial data isn’t “special” anymore: it’s just data. And that’s a good thing.

What This Means for You

If you work with spatial data, you should be seeking out datasets in these modern formats. Ask for GeoParquet, COG, or STAC-compliant APIs. Not because they’re trendy—but because they’ll save you hours of ETL time and let you get to the insight faster.

And if you produce data, start publishing it this way. The tools are already here: DuckDB, GeoPandas, GDAL, rio-cogeo, and Apache Sedona make this accessible even for small teams or solo developers.

A good starting point? Check out Source Cooperative where many of these cloud-native datasets are already published and ready to use.

Final Thought

Cloud-native geospatial is not just a new format—it’s a mindset. One that embraces openness, interoperability, and usability. It’s not about making spatial data cool. It’s about making it useful.

And if you want to go deeper, I break this all down in my LinkedIn carousel here (embed carousel).