The official source for Apache Sedona news, technical insights, release updates, and best practices in large-scale spatial data management.
The Mississippi wanders. Between the two edges of a single Sentinel-2 scene it travels 147.6 km to cover 103 km of ground, and its width swings from 131 m to almost 3 km. Every one of those numbers came out of raw pixels: Apache Sedona's raster operators built the river polygon, and ST_ApproximateMedialAxis drew the line down the middle of it.
Those deep-green grids are California's Central Valley — some of the most intensively farmed land on Earth, not as a camera sees it but as the NDVI vegetation index does: computed, pixel by pixel, by seven lines of NumPy. Sedona ran those seven lines on 484 satellite tiles in parallel, straight out of a public S3 bucket, and wrote the answer back as Cloud Optimized GeoTIFFs. That's the new move in Sedona 1.9.1: a plain Python UDF can take raster columns and return rasters. If the raster function you need isn't built in — your index, your QA rule, your model — write it.
Every raster team has the bucket. Thousands of GeoTIFFs accumulated from surveys, vendors, and pipelines — and no catalog. Which files cover this area? Which CRS are they in? Which ones were never converted to Cloud Optimized GeoTIFF? Until now, answering meant crawling the bucket with one-off scripts — or pulling rasters down just to inspect them.
Anyone can look at a map and see clumps. The interesting questions are the ones eyeballing can't answer: is that clustering statistically real? Exactly where is it significant? And which points genuinely don't belong?
OpenStreetMap is the world's map — every road, café, and coastline, edited by millions of people. It ships as .osm.pbf: a dense, compressed Protocol-Buffers blob of nodes, ways, and relations. Getting that into a cluster usually means a preprocessing detour through osmium or a staging database.
You have the shapes. Somewhere in a petabyte of satellite imagery are exactly the scenes that cover them. The hard part was never the analysis — it was the plumbing in between.
The hardest part of working with big rasters often isn't the analysis. It's just getting the file open.
In SedonaDB 0.4, we taught this Rust database to run spatial joins on your $1,500 gaming GPU's ray tracing cores, and it beats an H100.
The Apache Sedona community is excited to announce the release of SedonaDB version 0.4.0!
SedonaDB is the first open-source, single-node analytical database engine that treats spatial data as a first-class citizen. It is developed as a subproject of Apache Sedona. This release consists of 187 resolved issues including 26 new functions from 15 contributors.
Apache Sedona powers large-scale geospatial processing on distributed engines like Spark (SedonaSpark), Flink (SedonaFlink), and Snowflake (SedonaSnow). SedonaDB extends the Sedona ecosystem with a single-node engine optimized for small-to-medium data analytics, delivering the simplicity and speed that distributed systems often cannot.
The Apache Sedona community is excited to announce the release of SedonaDB version 0.3.0!
SedonaDB is the first open-source, single-node analytical database engine that treats spatial data as a first-class citizen. It is developed as a subproject of Apache Sedona. This release consists of 187 resolved issues including 36 new functions from 18 contributors.
Apache Sedona powers large-scale geospatial processing on distributed engines like Spark (SedonaSpark), Flink (SedonaFlink), and Snowflake (SedonaSnow). SedonaDB extends the Sedona ecosystem with a single-node engine optimized for small-to-medium data analytics, delivering the simplicity and speed that distributed systems often cannot.