Scaling Geospatial Analytics at S&P Global Energy: From Billions of Points to AI-Powered Map Agents

At S&P Global Energy, we transformed our geospatial analytics architecture to handle billions of records with query results in seconds — removing the need for separate geospatial databases and reducing costs using our Oil & Gas well production data. Architecture evolution: Unified single-source-of-truth on Delta Lake without redundant geospatial systems Databricks Spatial SQL with native spatial types enabling performant queries Databricks SQL Serverless delivering on-demand H3 aggregations Interactive Databricks app demo for large-well datasets. What’s next – AI geospatial agent (work in progress): Natural language map interactions powered by Agent Bricks Genie-driven text-to-SQL for dynamic map layer creation 50+ analytical tools like chart generation from spatial queries Attendees will learn practical patterns for scaling geospatial workloads, leveraging Spatial SQL with native spatial types, H3 indexing, and see how AI agents are reshaping spatial analytics workflows. Talk By: Hubert Boguski, Software Engineer, S&P Global ; Michael Johns, Lead Geospatial Product Specialist, Databricks ; Connect with us: Website: https://databricks.com X: / databricks LinkedIn: / databricks Instagram: / databricksinc Facebook: / databricksinc