Apache Sedona

Apache Sedona is an open-source spatial engine for processing geospatial data on a distributed cluster or a single node.

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2,000,000 Downloads per month

62,000,000 Number of downloads

2,200 Stars on github

200% Year over year growth

The Open Source Solution for High-Performance Geospatial ETL & Analytics

Apache Sedona extends distributed compute frameworks to add support for spatial data types, spatial indexing, and efficient spatial query processing using Spatial SQL.

With 300 vector and raster Spatial SQL functions and support for multiple distributed compute engines including Apache Spark, Apache Flink, and Snowflake, Apache Sedona brings the most comprehensive support of Spatial SQL to parallel and scalable data processing environments.

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Leverage resources to learn more about using Apache Sedona to solve your large-scale spatial data problems.

During this office hour, we'll coverthe features of a number of releases across the Apache Sedona ecosystem including- Apache Sedona 1.8.1- SedonaDB 0.2.0- SpatialBench 0.1.0

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Apache Sedona Community Office Hour: February 2026

Topics covered:• Apache Sedona 1.8.1 • SedonaDB 0.2.0 • SpatialBench 0.1.00:00 Introduction 3:57 What Is SedonaDB? 6:59 Apache Sedona Community Growth 9:50 Sedona Flink SQL Module for Enhanced Flink Integration 11:40 STAC Authentication Support in Python & Scala APIs 13:57 ST_ApproximateMedialAxis & ST_StraightSkeleton: Why They Matter 19:24 SpatialBench Overview: Components & Results 22:19 Sedona Performance Benchmarks: Databricks Serverless SQL vs. Sedona 28:03 SedonaDB Demo Spatial Query Benchmarking on Databricks with SpatialBench: https://sedona.apache.org/latest/blog/2026/01/08/spatial-query-benchmarking-databricks/

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Apache Sedona Community Office Hour: January 2026

Topics covered:- Highlights from the new Apache Sedona 1.8 release- A sneak peek at what’s next in Apache Sedona 1.8.1- Latest status report on ST_Transform for better accuracy- ST_Transform in SedonaDB explanation and live demoCheck out the Cloud Native Geospatial Analytics with Apache Sedona: https://bit.ly/3T9DqGa

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Apache Sedona Community Office Hour: November 2025

We’re celebrating Apache Sedona 1.8.0 release and the launch of SedonaDB & SpatialBench with a special Apache Sedona Community Office Hour!Here’s what we’ll cover: - What’s new in Apache Sedona 1.8.0 - The launch of SedonaDB with examples & demos - A deep dive into SpatialBench and its designCheck out SedonaDB here: https://sedona.apache.org/sedonadb/latest/

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Apache Sedona Community Office Hour: October 2025

In this session, we'll cover: - New Area of Interest API in ST_Transform- GeoPandas API progress- Sneak peek at 1.8.0 release- Geography type support update- New Well-Known Locations library

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Apache Sedona Community Office Hour: September 2025

Features of Sedona 1.8.0 covered:- GeoPandas API for Sedona- Running Sedona on PyFlink- Comprehensive Spark 4.0 support- Geography type integrationDownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG

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Apache Sedona Community Office Hour: July 2025

During this office hour, we covered the upcoming Apache Sedona 1.8.0 release:- GeoPandas API on Sedona- Sedona on PyFlink- Spark 4.0 support- Sedona vectorized Python UDF- GeoParquet, and Parquet Geo types0:00 Introduction2:11 Overview of Sedona System Architecture6:45 What’s new in 1.7.1 release10:26 OpenStreetMap PBF Reader12:44 Sneak Peak: Sedona 1.7.2 Release 13:35 Looking Ahead: Sedona 1.8.0 Release16:56 Why We’re Support Java 1121:11 Example of PyFlink Support22:38 Overview of GeoParquet24:30 What is Apache Iceberg?27:12 Working With Iceberg Geo Types33:49 Working With Parquet Geo Types35:44 Final Thoughts & Q&ADownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG

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Apache Sedona Community Office Hour: June 2025

For this month's office hour, we covered:- Broadcast join support for distributed KNN Join- STAC catalog & OpenStreetMap (OSM) PBF reader- Preview of 1.8.0: GeoPandas API, PyFlink support & Spark 4.0 compatibility- Live demo: Using Sedona with Overture Maps dataDownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG

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Apache Sedona Community Office Hour: May 2025

This month, we covered Sedona 1.7.1 release: - SQL interface for GeoStats (ST_DBSCAN, ST_GLocal, ST_LocalOutlierFactor) - Broadcast join support for distributed KNN Join - STAC catalog & OpenStreetMap (OSM) PBF reader - New ST functions like ST_RemoveRepeatedPoints0:00 Introduction2:27 Sedona System Architecture8:21 What’s New in Sedona 1.7.1 Release10:50 GeoSTATS12:25 Broadcast kNN Join14:59 OpenStreetMap PBF Reader17:16 GeoParquet with Balanced Partitioning20:09 GeoArrow Support in Sedona DataFrame21:31 Faster Rasterization24:14 STAC Catalog reader - Feng Zhang29:07 STAC Item Schema30:57 DF/SQL Filter Pushdown34:38 Example: Using STAC Reader with Your Notebook42:33 Q&ADownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG

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Apache Sedona Community Office Hour: April 2025

Each month we host the Apache Sedona community office hour as a way to connect the Apache Sedona community and cover the latest project updates, a focus topic presentation, time for open discussion and Q&A, and information on the roadmap and future plans. Topics covered:What’s new in Sedona 1.7.0- KNN Join- GeoStats- DataFrame readers (shapefiles, GeoPackage). Will potentially invite Kristin to give a presentation about the Shapefile readerFeatures in upcoming new 1.7.1 release- GeoStats SQL interface- STAC reader- Iceberg Geo reader / writer in Sedona- Parquet Geo reader / writer in SedonaCommunity development- Improvement on docs- Enable GitHub discussion- Encourage to use GitHub issues instead of JIRADownload the O’Reilly book: https://bit.ly/3T9DqGa Join Apache Sedona Discord channel to engage with the community: https://bit.ly/460BSWG

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Apache Sedona Community Office Hour – March 2025

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Apache Sedona Community Office Hours – February 2025

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Apache Sedona Community Office Hours – December 2024

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Apache Sedona Community Office Hours – November 2024

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Apache Sedona Community Office Hours – September 2024

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Apache Sedona Community Office Hour – August 2024

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Apache Sedona Community Office Hour – June 2024

Each month we host the community office hour as a way to connect the Apache Sedona community and cover the latest project updates, a focus topic presentation, time for open discussion and Q&A, and information on the roadmap and future plans.It’s an excellent opportunity for the community to engage and contribute to Apache Sedona’s development.This time we'll be covering:* What's new in Apache Sedona 1.5.2* What's new in Apache Sedona 1.6.0* Apache SIS integration* Creating Sedona User Defined Functions (UDFs) with Shapely, Rasterio, and NumPy

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Apache Sedona Office Hours – May 2024

Connect with Apache Sedona across platforms and networks.

Github

The Apache Sedona GitHub repository is the home of Sedona’s open-source code

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Apache Sedona YouTube Videos

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dockerhub

The official Apache Sedona Docker image is the easiest way to get started with Apache Sedona

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Review the latest on Apache Sedona across docs, blogs, and release notes.

Apache Sedona Documentation

Find tutorials and reference docs in the official Apache Sedona documentation

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Apache Sedona On The Wherobots Blog

Many of the posts on the Wherobots blog are technical examples using Apache Sedona

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release notes

Keep up to date with new features in each release in Apache Sedona’s release notes

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Get Involved

Join the Apache Sedona community and be a part of the modern geospatial analytics ecosystem. There are many ways to be involved.

Learn how to use our new Model Context Protocol (MCP) server to streamline your spatial data workflows. This allows you to interact with your spatial catalogs and databases directly through an AI agent.In this walkthrough, we cover:- Catalog Exploration: Watch the MCP server navigate hierarchies to find specific datasets (like NYC Taxi data) across professional and custom catalogs.- Data Introspection: Perform live queries against massive datasets to get instant insights, such as counting 185 million taxi pickups in seconds.- Agentic Q&A: See how the server handles complex natural language requests, like finding "burger joints near the Space Needle," by inferring locations and schemas automatically.- Notebook Generation: Learn how to use the MCP server to write and validate Python/Jupyter notebooks for heavy-duty spatial joins and large-scale data processing in Wherobots Cloud.Try the MCP server: https://docs.wherobots.com/develop/mcp/mcp-server-installation0:00 – Introduction to the Wherobots MCP Server0:25 – Catalog Exploration: Searching for spatial datasets1:40 – Data Exploration: Running live queries against the database3:11 – Agentic Q&A: Natural language spatial searches (Space Needle Example)4:40 – Generating Jupyter Notebooks for large-scale processing6:45 – Security & Permissions: Understanding the auto-approval step7:55 – Model Choice: Why Claude 4.5 Sonnet is recommended9:10 – Running the generated notebook in Whereabouts Cloud10:40 – Final thoughts and how to get started

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Wherobots MCP Server Demo

Wherobots Cloud is a fully managed cloud platform that enables developers and data scientists to efficiently manage their spatial analytics and AI pipelines in the cloud. The core of Wherobots Cloud is powered by SedonaDB, a scalable geospatial analytics database engine. SedonaDB is built on a distributed compute architecture which enables scalable computation of massive datasets without sacrificing speed. With an architecture that separates the compute layer from the storage layer SedonaDB is truly cloud-native.SedonaDB is built upon the open-source Apache Sedona project that provides the foundation for scalable geospatial analytics. By leveraging SedonaDB in Wherobots Cloud developers and data scientists can take advantage of optimized query processing, a data lakehouse architecture built on Havasu - an Apache Iceberg-compatible spatial table format- with a self-service fully-managed cloud environment.Resources:* Create a free Wherobots Cloud account: https://www.wherobots.services* Join the Wherobots Online Community: https://community.wherobots.com/* Wherobots Cloud Documentation: https://docs.wherobots.services* Wherobots Blog: https://wherobots.com/blogs/* Learn more about Wherobots: https://wherobots.com/

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Overview Of The Wherobots Cloud Platform

The Wherobots Notebook Environment is the main development interface for developers and data scientists working with SedonaDB & Wherobots Cloud based on Jupyter. In this video we take a look at configuring and starting a Wherobots Notebook runtime as well as some of the example notebooks available by default, and how to use version control in our notebook environment.Resources* Create a free Wherobots Cloud account: https://wherobots.com/wherobots-cloud/* Wherobots Community Site: https://community.wherobots.com/

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Introduction To The Wherobots Notebook Environment

This video covers loading files of various formats (CSV, GeoJSON, Shapefile, GeoTIFF) in Wherobots Cloud, working with AWS S3 cloud object storage, and writing the results of geospatial analysis as GeoParquet using Apache Sedona.Create a free Wherobots Cloud account to get started with large-scale geospatial analytics with Apache Sedona: https://www.wherobots.services/Chapters0:00 Loading A CSV File From a Public S3 Bucket2:05 Uploading a GeoJSON File Using The Wherobots Cloud File Browser4:44 Uploading Files To Wherobots Cloud Using The AWS CLI8:50 Working With Raster Data - Loading GeoTiff Raster Files10:06 Writing Files With Wherobots Cloud

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Working With Files In Wherobots Cloud

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Geospatial Python: Raster and vector data loading in Apache Sedona [Tutorial]

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Wherobots 101: Mastering Scalable Geospatial Data Processing

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Wherobots 102: Reading and Processing Cloud Native Geospatial Data

In this session, you'll learn how to seamlessly integrate Python and Wherobots to perform advanced spatial joins and analyses on geospatial data.Webinar Highlights:- Learn how to join datasets using spatial predicates like ST_Intersects to combine facilities with administrative boundaries—ensuring only relevant spatial relationships are processed.- See how to leverage the powerful ST_AKNN function to efficiently find the k-nearest neighbors for each facility. We’ll explore a Pythonic approach to identify the closest centroids to your facilities, ensuring a highly optimized query without the overhead of expensive cross joins.- Understand how to apply spatial filters to extract a targeted subset of your data. We'll walk through filtering facilities that fall within a specified geographic boundary.- Explore strategies like repartitioning by geohash to improve performance and reduce data shuffling during spatial joins, making your spatial queries scalable for large datasets.- Finally, learn how to visualize your spatial join results interactively using SedonaKepler and SedonaPyDeck, turning complex geospatial data into actionable insights with rich, interactive maps.Timestamps00:00 Introduction 03:01 Introduction to Wherobots, Apache Sedona and product architecture08:16 Performance benchmarks: Wherobots vs. Sedona, BigQuery and Snowflake09:55 Create account and get started with Wherobots | Start a notebook, setting runtime, adding Python libraries, cloud storage, and spatial catalog18:40 Start demo | Connect to runtime and notebook, importing functions from PySpark20:48 Load data | Polygons from Overture Maps and points from Foursquare Open Places Dataset23:49 Standard Spatial Join (Pythonic Approach)27:32 Spatial aggregate | Efficiently count points in each polygon 29:13 K Nearest Neighbor Join 37:22 Advanced optimization techniques | Cluster data using Geohash 47:28 Visualize Spatial Join results with SedonaKepler and SedonaPyDeck48:11 Summary and Q&ASign up for / Get Started Wherobots for free: https://bit.ly/3Q9PcPe

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Spatial Joins at Scale: Unlocking Advanced Geospatial Analytics with Wherobots

Zonal statistics—like calculating average elevation or temperature over custom geometries—is one of the most powerful ways to combine raster and vector data. But when it comes to scaling up beyond a few polygons, traditional GIS tools often hit a wall.In this live session, we’ll show you how Wherobots makes large-scale zonal stats simple, fast, and scalable using RS_ZonalStats, a powerful function built for modern spatial SQL. You’ll see how easy it is to load vector features (like buildings or land parcels) and join them with cloud-hosted raster layers to compute meaningful insights—without slowdowns.We’ll walk through:- How to prepare and load both raster and vector data- Using RS_ZonalStats to compute min, max, mean, and more- A real-world demo with Overture Maps and elevation data from AWS- Tips for optimizing and scaling your raster analysis workflowsWhether you’re analyzing terrain, land cover, or climate data, this is your shortcut to making raster analysis work at any scale.0:00 Introduction4:38 Overview of RS_ZonalStatsAll and ST_Isochrones7:48 Set up10:17 Spatial join between raster and polygon dataset13:15 Accessing and preparing raster data 18:59 Load isoochrone polygons from Iceberg 21:16 Compute zonal statistics26:05 Results and efficiency28:41 Further exploration and Q&ASign up for / Get Started Wherobots for free: https://bit.ly/3Q9PcPe

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Scaling Zonal Statistics: Fast Raster + Vector Joins with Wherobots

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