Dewey Dunnington

@paleolimbot.bsky.social

Scaling spatial computing at Wherobots, Inc. ApacheArrow PMC, #gischat, #rstats, ex Voltron Data.

We're excited to announce a new release of SedonaDB, and this one is a big one! Between native DataFrame APIs in Python and R, a brand new Geography implementation, GPU accelerated joins, and a raster/ND-array foundation, it's hard to know where to start.

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dbplyr 2.6.0 is here! This release leaned on Claude Code to clear a TON of smaller issues, freeing up time for the big stuff: brand-new ADBC and JDBC backends, new translations, and a new sql_dialect() to cleanly decouple connection from SQL dialect. Read more: opensource.posit.co/blog/2026-06...

dbplyr 2.6.0

dbplyr 2.6.0 is now on CRAN, with new ADBC and JDBC backends, IBM DB2 support, more translations, and a bunch of minor fixes and improvements.

opensource.posit.co

Can your current spatial SQL engine ST_💩()? We didn't think so! As part of our ongoing attempt to innovate on spatial SQL in Apache Sedona, this we're proposing emoji shorthands for most functions which (1) improve expressiveness and (2) helps more SQL fit on one line.

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I tend to be a CRAN-appreciator. But wow, R-Universe is such a useful project. As a developer, I can: - Experiment with new and creative approaches that would never get accepted on CRAN; - Have cross-platform binaries for my packages built within an hour (usually) of a GitHub push

The fastest operation is the one you don’t have to do. When a database natively supports @arrow.apache.org, ADBC can speed up fetching and ingestion by eliminating costly row/column conversions. How much faster is it in practice? We ran some benchmarks to find out. Link below 👇

An abstract hyperspace warp image inspired by the comedic "going plaid" effect from the 1980s cult film "Spaceballs".

We're chuffed to announce Apache SedonaDB 0.3.0! This release features a rewritten join that supports larger-than-memory spatial/KNN joins courtesy of Kristin Cowalcijk, new functions, parameterized SQL queries, GDAL/pyogrio reads, GDAL/sf based reads in R, and the beginnings of an R DataFrame API!

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