Apache Sedona vs. Big Data: Solving the Geospatial Scale Problem

We’ve all been there. You hit “run” on a spatial join query, walk away to get coffee, come back, and it’s still running. Or worse, your machine has crashed completely.

Processing geospatial data isn’t just about volume; it’s about complexity. A single polygon can be megabytes in size, and checking spatial relationships (like intersections) is computationally expensive. If you try to force this into standard big data computing models, you hit a wall.

In this episode, Matt Forrest sits down with Jia Yu, the co-creator of Apache Sedona and co-founder of Wherobots, to discuss how they built the engine that solves these exact problems at scale.

Here are the key takeaways from their conversation on modernizing geospatial architecture.

1. Why Geospatial Data Processing is Unique

Jia explains that spatial data suffers from a “multimodality” problem. It isn’t just simple rows and columns; it’s trajectories, polygons, satellite imagery, and LiDAR data all mixed together.

Standard databases treat data as independent rows, but spatial data has critical physical relationships. If your architecture doesn’t account for the shape and proximity of data—specifically the complex geometries involved—your processing power evaporates.

2. Optimization via Distributed Spatial Partitioning

The secret sauce of Apache Sedona isn’t just that it uses a cluster; it’s how it uses the cluster.

In standard distributed computing, you might partition data by hash or date. In spatial computing, that fails. You need to preserve spatial proximity. Sedona groups data that is geographically close onto the same machines. This means when you run a query for a specific region, the system performs a targeted scan rather than shuffling data across the entire cluster.

3. The Power of Spatial SQL and Python APIs

Sedona started strictly as a Java/Scala project. While powerful, adoption was initially slow. Jia notes that the explosion in growth—to millions of downloads—happened only after they introduced Spatial SQL and Python APIs.

The Lesson: You can build the fastest engine in the world, but if it doesn’t fit the user’s existing workflow, it won’t be adopted. Today, roughly 80% of Sedona users rely on Python and SQL to integrate spatial analysis into their existing pipelines.

4. Building a Spatial Lakehouse with Apache Iceberg

For decades, geospatial data has been trapped in proprietary file formats, requiring specialized drivers to read. Jia advocates for the Spatial Lakehouse: decoupling compute from storage and using open formats like Apache Iceberg.

This architecture allows you to store your data once in an open standard and point any engine (Sedona, Databricks, Snowflake) at it without building complex, brittle ETL pipelines.

5. Integrating Spatial Intelligence into AI Models

Current AI models (LLMs) are brilliant at language but often blind to the physical world. If you ask an LLM to find the “nearest restaurant to these 10 points,” it frequently fails because it lacks the concept of distance and geometry.

Tools like Sedona are becoming the “spatial brain” that feeds physical context into AI models, bridging the gap between text generation and real-world spatial reasoning.

How to apply this

If you are dealing with growing spatial datasets, here is how to modernize your stack:

• Stop crashing your laptop: If you are struggling with local Python scripts, look into SedonaDB. It’s a new single-node engine (built on Rust) that offers Sedona’s optimization without needing a full cluster setup.
• Ditch the data silos: Move your data into open table formats like Apache Iceberg. This future-proofs your architecture and prevents creating duplicate copies of data for every different tool you use.
• Think architecturally: Don’t just blindly call functions. Understand how your data is partitioned. If you are performing heavy spatial joins, ensure your data is indexed and partitioned by location to minimize network shuffling.