Apache Sedona Tutorial: Scalable Spatial Joins and Geospatial Processing with Spark
If you’ve ever tried to run a spatial join on millions of features and watched your machine cry for help, it’s time to level up with Apache Sedona.
In this new video, I walk through a complete hands-on tutorial for using Sedona in Python via JupyterLab. We’ll load spatial data, perform scalable spatial operations, and explore what makes Sedona a powerful choice for modern geospatial data processing.
What You’ll Learn
- How to set up a local Sedona environment with Spark
- Performing spatial joins and transformations at scale
- Running everything inside a familiar Jupyter notebook
Whether you’re coming from GeoPandas or PostGIS, this is a great entry point to start working with distributed spatial computing in Python.
Resources
- 📦 GitHub Repo (Notebook + Setup Instructions): https://github.com/mbforr/sedona-tutorial
- 🗂️ Download the sample data used in the tutorial
Sedona makes it possible to scale up your geospatial work without giving up the tools you love. If you’ve been wanting to bridge the gap between desktop GIS and big data, this is your next step.
