It's been almost a month since Geoparquet 1.0.0 was released, and the amount of tools and data already embracing the spec is incredible. Read cloudnativegeo.org/blog/2023… for details of all that's going on, and please join and help build an incredible ecosystem!

First blog post in a long time! I started writing a post ~2 years ago on adventures counting 130M U.S. buildings by zipcode and finally circled back to write it up. Everybody is a winner really, but @duckdb @IbisData , @ApacheArrow, and @GeoParquet were essential throughout!

BigQuery now supports @GeoParquet when exporting Geography values. cloud.google.com/bigquery/do… Metadata would have encoding=WKB, orientation=counterclockwise, edges=geodesic.

The initial goal of @GeoParquet might have been modest. Its ultimate goal is a future where #geospatial data is integrated across the data landscape. Read @opencholmes & @jatorre's blog post to learn more about breaking the 'GIS' data silo. cloudnativegeo.org/blog/2024…

Exciting to see @OvertureMaps leveraging @protomaps PMTiles for fast map visualization & @GeoParquet for analysis-ready data downloads - all client-side 🤯! A great example of open-source tech empowering geospatial exploration. #OpenData #Geospatial

The @GeoParquet 1.1 revision is out, adding support for spatial partitioning and native GeoArrow geometries Both have potential to massively speed up working with very large geospatial datasets. Next step is ensuring the ecosystem works with this version github.com/opengeospatial/ge…

GDAL 3.9 was released today with a bunch of updates for @GeoParquet, including support for spatial filtering in the upcoming GeoParquet 1.1 version! Credit to @EvenRouault and other GDAL contributors 🙇‍♂️! github.com/OSGeo/gdal/blob/v…

Listen in for a discussion on what @GeoParquet solves and why you should (or shouldn't) consider using it! We cover how GeoParquet is cloud native, how its compression makes reading and writing faster, and how it integrates with GeoArrow for fast in-memory computing.

In case you missed the Lonboard livestream, it's on YouTube! Check it out for a discussion on spatial data visualization in Python, plus some nuggets of @GeoParquet and GeoArrow. piped.video/watch?v=NAjabS0G…

🚀 Progress towards supporting efficient spatial queries in the @GeoParquet 1.1 spec, in Python, JavaScript, and Rust! It took 12 seconds to fetch this data from the 2.3 BILLION row Overture buildings dataset

A great new project that has GeoParquet at its core. And lots of new GeoParquet datasets are up at beta.source.coop/fiboa/

If you missed the @GeoParquet Community Day in San Francisco, you can now catch up on all the lightning talks, demos, and hands-on workshop at your leisure! 📽️ Watch Now: piped.video/playlist?list=PL…

Awesome turnout today at the @cloudnativegeo GeoParquet Community Day in San Francisco 🤩

@felt now supports @GeoParquet! This was my first feature request for Felt - I'm psyched it's come through. It's great to see more and more web tools support the format. And I've heard the Felt team is embracing GeoParquet for more internal use as well.

Learn how @GeoParquet can enable efficient storage and retrieval of geospatial data with a hands-on example in the latest post on the @wherobots blog: wherobots.com/exploring-glob…

@ESIPfed's cloud computing cluster speakers: ▪️Nikki Tebaldi- Moving to the Cloud: Migrating a data processing workflow to the AWS Cloud ▪️ Raphael Hagen- Using Kerchunk to make it easier to use climate data ▪️ Kyle Mann- Cloud Optimized Point Cloud ▪️ Kyle Barron- @GeoParquet

Folks, Merry Christmas and Happy New Year! 🎅 🎄 We @wherobots thrilled to announce the Havasu spatial table format, an @ApacheIceberg based innovation integrated into SedonaDB. For more insights, visit our blog: wherobots.com/havasu-a-table… As Havasu garners interest among Wherobots Cloud users, we've prepared FAQs to clarify its features and uses. Q1: What is Havasu format? Havasu is a spatial data lake format powered by Iceberg, comprising a specification and implementation. The open-source specification outlines storage methods for spatial data within Iceberg's framework (Apache-2.0 license): github.com/wherobots/havasu. SedonaDB, a proprietary database engine based on @ApacheSedona (sedona.apache.org/), implements Havasu and Iceberg. Q2: Difference between Havasu and Apache Iceberg? Are you reinventing the wheel? Definitely not! Havasu is nothing but an extension to the Apache Iceberg format. It adds geometry and raster data types, spatial column metadata, and specific spatial data encoding in Parquet files, without altering Iceberg's table file structure. Q3: Havasu vs. @GeoParquet file format? Havasu uses the GeoParquet standard for geometry in Parquet files, ensuring compatibility with Parquet / GeoParquet readers. Its raster data is stored in a Parquet-native array<struct> format, supporting both integers and floating points. Q4: Benefits of Havasu format? Spatial DBMS Experience: Offers ACID-compliant spatial transactions, supporting concurrent insertion/deletion/query/update operations across multiple applications on the same Havasu table, with SedonaDB enforcing spatial integrity at the ingestion time. Performance: Reduces the need for parsing and transforming spatial data at the application level, thanks to Havasu's efficient data handling. Spatial Filtering and Query Optimization: Utilizes spatial statistics for Parquet files to prune irrelevant data files and optimize spatial join queries. Inherits all Apache Iceberg features. Q5: Is Havasu open-source? I don't want to run into the vendor lock-in situation. Yes, Havasu's specification is open-source, building on Apache Iceberg to avoid vendor lock-in. Data is stored on AWS S3 in Parquet format, readable by any Parquet reader. SedonaDB ensures Iceberg compatibility for all non-spatial applications. Our plans to integrate some Havasu features into Apache Iceberg are underway. #geospatial #bigdata #cloudarchitecture #parquet

Amazing stuff! Love the add of GERS ID for all records. WOW.

Calling all #SpatialData enthusiasts! Join us in #SanFran on Jan 30 for our 1st-ever @GeoParquet Community Day! Big thanks to @wherobots for being a convening sponsor. ℹ️ Learn more, register, become a speaker, or explore sponsorship opportunities here: cloudnativegeo.org/blog/2023…

Just wrapped up the final lecture of my Spatial Data Management course this semester, diving into lightning-fast ⚡️analysis of massive geospatial 🌎 datasets with the power of DuckDB 🦆 🎥 Video: piped.video/VSXTyKWmDVU 📒 Notebook: geog-414.gishub.org/book/duc… 🎶 Playlist: bit.ly/geog-414 #geospatial, #duckdb, #dataviz #geemap #leafmap

🌟 This #TutorialTuesday, we're exploring another noteworthy tutorial by @giswqs, including a demo for visualizing the National #Wetlands Inventory dataset on @source_coop in @GeoParquet format. 📓 leafmap.org/notebooks/84_rea… 🗃️beta.source.coop/repositorie…