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Apache Sedona Tutorial

Welcome to the Apache Sedona Tutorial 🗺️ – a hands-on guide to large-scale spatial data processing using Apache Sedona with Python and Jupyter.

This repo contains a Jupyter Notebook that walks through how to use Sedona for scalable spatial operations.

🚀 Getting Started (Local Setup)

This tutorial uses UV for package management and sdkman to install Java.

1. Install Java (via SDKMAN)

curl -s "https://get.sdkman.io" | bash
source "$HOME/.sdkman/bin/sdkman-init.sh"
sdk install java 17.0.13-zulu

# Confirm install
java -version
echo $JAVA_HOME

2. Install UV

pip3 install uv

# Alternatively, via shell script
curl -LsSf https://astral.sh/uv/install.sh | sh

3. Install Python dependencies

uv pip install -r pyproject.toml

4. Register Jupyter Kernel

uv run ipython kernel install --user --name=SedonaDemo

5. Run Jupyter Lab

uv run --with jupyter jupyter lab

📂 Data

This notebook depends on sample geospatial data. You can download a zip file of all the data and place it in the main directory after download.

👉 Download the data here

Please download and place the data in a local folder before running the notebook.

🛠 What's Inside

  • 🔹 Coordinate reference system transformations
  • 🔹 Spatial joins
  • 🔹 Raster + vector handling (with extensions)
  • 🔹 Integration with SparkSQL

💡 Requirements

  • Python 3.9+
  • Java 17
  • Jupyter Lab
  • Apache Sedona (via PySpark)

📬 License

This project is licensed under the MIT License.

Made with ❤️ for spatial data engineers.