Last week the team from Apache Sedona released SedonaDB, a new tool that makes spatial processing fast and also treats spatial data like a first class citizen. Or read a different way it is really fast for doing spatial analytics. It uses Python and SQL to analyze data and can also output data in GeoParquet […]
Storage and formats are the foundation. Pipelines make them practical. But storage and processing architectures determine what’s actually possible at scale. We’ve talked about cloud storage enabling modern geospatial workflows and the formats that make data queryable. We’ve covered the pipelines that get your data into those formats. But there’s a bigger architectural question that […]
The geospatial technology landscape is changing fast. What used to be a world of shapefiles, desktop software, and siloed workflows is now becoming cloud-native, AI-driven, and analytics-focused. This shift isn’t just technical—it’s reshaping how geospatial professionals build, analyze, and share data. In this post, I’ll break down the key trends shaping the modern geospatial data […]
In the world of spatial data, building pipelines often feels like duct-taping together cron jobs, brittle scripts, and whatever cloud service happens to be in reach. Whether you’re transforming geospatial imagery, generating features for machine learning, or managing time-sensitive datasets like weather and river gauges chances are, your pipelines could use an upgrade. In the […]
Having the right formats is one thing. Getting your data into those formats reliably, at scale, and on schedule is another thing entirely. You can read all you want about GeoParquet and Zarr and COG, but if you can’t create a repeatable process to convert your legacy shapefiles to GeoParquet or your NetCDF files to […]
