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Learn how the Global Water Security Center achieved a 99% reduction in query times on massive geospatial datasets by implementing a modern lakehouse architecture with Databricks. Discover the technical transformation that reduced analysis time from multiple business days to seconds when processing approximately 500,000 raster files containing over 500 billion data points for U.S. Department of Defense environmental science applications. Explore the implementation of key technologies including Databricks Auto Loader for automated data ingestion, Spark Streaming for real-time processing, Databricks Spatial SQL for geospatial queries, H3 geospatial indexing for efficient spatial data organization, and Databricks Liquid Clustering for optimized data storage and retrieval. Understand how pre-computed tables enable data scientists to execute complex queries in seconds rather than days, dramatically improving analytical capabilities and allowing more time for deeper data analysis. Examine the complete CI/CD implementation using Databricks Workflows, Databricks Asset Bundles, Git, and Git Actions to support seamless deployment across multiple workspaces, presented by Chris Crawford from Databricks and Hobson Bryan from the Global Water Security Center.
