Join the Sky to the Ground: Spatial Joins over STAC Catalogs
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.
七月 2026¶
Open Huge GeoTIFFs Without the 2 GB Wall
The hardest part of working with big rasters often isn't the analysis. It's just getting the file open.
SedonaDB 0.4: GPU-Accelerated Spatial Joins
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.
SedonaDB 0.4.0 Release
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.
SedonaDB 0.3.0 Release
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.
Apache Sedona 2025 Year in Review
2025 was a milestone year for Apache Sedona. We made major progress in distributed spatial analytics on Spark, Flink, and Snowflake, launched a new single-node engine called SedonaDB, and pushed forward benchmarking and open geospatial data standards.
This post summarizes the most important highlights from the Apache Sedona ecosystem in 2025.
Spatial Query Benchmarking on Databricks with SpatialBench
Recently Databricks announced that Serverless SQL users "will see up to 17x faster performance compared to classic clusters with Apache Sedona installed." Unfortunately, Databricks didn’t speak to the cost of the results. It is also an apples-to-oranges comparison because the serverless compute shape and quantity that Databricks deployed to generate the 17x performance difference was not shared. Their result was also limited to specific query configurations.
We saw an opportunity to address these issues using SpatialBench, a new benchmarking framework for spatial queries. Because we are comparing different infrastructure types, our benchmark normalizes on price-performance rather than performance alone, while providing what we believe is a more comprehensive benchmarking result.
We found that only one of the simpler SpatialBench queries (#2) tested on Databricks SQL Serverless had price-performance aligned with the Databricks claim. However, we found that Sedona excelled in most other queries, delivering up to 6x better price-performance, while offering more query coverage.