Learn how analytical databases make the most of your laptop to make existing workflows faster and larger workflows possible using SedonaDB, a database built for spatial from the ground up.
It’s not just you…working with large spatial datasets in Python is awkward. Examples include data that is too big to fit into memory, too big to fit comfortably on a local hard drive, or takes minutes to even load into Python using existing tools. If you’re working with spatial data in Python and have waited more than 10 seconds for something to finish, this workshop is for you (and, spoiler alert: you may be waiting too long).
Launched in September 2025, SedonaDB is an analytical database engine built on DataFusion that integrates spatial concepts from its internals (e.g., native data types, joins, and statistics handling) to its interface (e.g., where documentation for hundreds of spatial functions is built in to the interface). SedonaDB offers a cohesive set of principles across Python, R, and SQL designed for compatibility with existing tools (e.g., PostGIS, GeoPandas, GDAL, DuckDB) to facilitate knowledge transfer from both geo-native and database-native users alike.
This workshop is geared towards participants that have familiarity with Python that are interested in learning spatial database and cloud native concepts to work with spatial data that is just a little too big to be comfortable on their laptop. We will cover the building blocks of analytical databases (e.g., tables, joins, scalar functions, and aggregate functions) and how spatial extensions implement them to effectively utilize all of the processors and all of the memory available on your laptop using hands-on examples against real data.
We will use SedonaDB in the workshop as a teaching tool, but will also include a short module ensuring the concepts we apply can be transferred to similar tools such as PostGIS, DuckDB and Apache Sedona for Spark, which all implement a similar mapping of spatial concepts to analytical database implementations.