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Blog
28 posts on Apache Sedona.
Spatial Graph RAG for the Physical World
Introduction RAG (Retrieval Augmented Generation) has addressed one of AI’s biggest challenges for enterprise users: missing or hallucinating empirical business and real world context . Instead of generating answers from nothing, RAG retrieves relevant documents and feeds them to the model as context. It works. Ask an AI about your company’s Q4 revenue, and RAG […]
Streaming Spatial Data into Wherobots with Spark Structured Streaming
Real-time Spatial Pipelines Shouldn’t Be This Hard (But They Were) I’ve been doing geospatial work for over twenty years now. I’ve hand-rolled ETL pipelines, babysat cron jobs, and debugged more coordinate system mismatches than a person should reasonably endure in one lifetime. So when someone says “streaming spatial data,” my first reaction used to be […]
Raster Processing at Scale: The Out-of-Database Architecture Behind WherobotsDB
Learn how WherobotsDB's out-of-database architecture processes terabyte-scale satellite imagery, elevation models, and sensor data at scale, enabling zonal statistics, raster algebra, and planetary-scale AI inference without custom infrastructure.
Introducing Scalability for GeoPandas in Apache Sedona
Learn about the new GeoPandas API for Apache Sedona, now available in Wherobots. This new API allows GeoPandas developers to seamlessly scale their analysis beyond what a single compute instance can provide, unlocking insights from large-scale datasets. This integration combines the Pythonic GeoPandas API with the distributed processing power of Apache Sedona.
Introducing SedonaDB and SpatialBench for Apache Sedona
The spatial-first query engine and benchmarking framework for Apache Sedona
Raster Spatial Joins at Scale: Google Earth Engine and BigQuery vs Apache Sedona and Wherobots
Perform spatial joins at scale and zonal statistics with vector and raster data using Google Earth Engine & BigQuery vs. Apache Sedona & Wherobots. Compare performance, architecture, and geospatial for geospatial analysis.
Wherobots, Sedona, and GeoPandas are better together
Discover the synergy of Wherobots, Sedona, and GeoPandas for geospatial data computation. This article details their execution differences, showcases the Sedona GeoPandas API, and highlights their combined potential for efficient large-dataset analysis.
Benefits of Apache Iceberg for geospatial data analysis
Apache Iceberg v3 supports the geometry type which allows the geospatial community to take advantage of amazing Iceberg features like reliable transactions, DML operations (deletes & upserts), time travel, versioned data, schema enforcement, schema evolution, and much more. This post shows how to use Apache Iceberg with the Sedona engine and explains how these features benefit spatial computations.
The Spatial Intelligence Newsletter: Map Matching, Spatial Joins, ML for EO, Cloud-Native Geospatial and More
Welcome back to the latest edition of the Spatial Intelligence Newsletter! From map matching to spatial joins and cloud-native geospatial, our team has been busy brewing up some exciting developments here at Wherobots!
This Month in Apache Sedona: November Edition
Welcome to the November edition of This Month in Apache Sedona! In this issue, you'll find the latest news about Apache Sedona and Wherobots. Learn about our newest release, which includes KNN Join, GeoStats, and DataFrame-based readers. Plus, we have plenty of exciting upcoming events to share!
How to shift Apache Sedona on Spark workloads to WherobotsDB
Wherobots customers are realizing up to a 20x performance increase and significant cost savings by shifting their Apache Sedona workloads into Wherobots. This guide shows you how easy it is to migrate Apache Sedona workloads into WherobotsDB, and focuses on best practices for Apache Sedona migrations from Amazon EMR, AWS Glue, and Databricks.
Introducing kNN Join for Wherobots and Apache Sedona
We are excited to introduce the k-Nearest Neighbors Join (kNN Join) for WherobotsDB and Apache Sedona 1.7.0. With the kNN Join, you can efficiently find the closest entities to your points of interest from datasets at scale. We’ve released two types of kNN Joins: the Exact kNN Join and the Approximate kNN Join. With these two kNN Join options, you can efficiently find the closest entities to your points of interest from datasets at scale, and trade off performance versus accuracy needs.
