Cloud Native Patterns for Geospatial Analytics and AI
summary
Argues for moving geospatial compute to cloud data lakes and running distributed jobs on Databricks or VM clusters rather than downloading data to laptops, demonstrating reading thousands of GML files (INSPIRE polygons) into memory with PySpark UDFs and writing analytics-ready Parquet, with a path to GeoParquet to preserve geometry. Shows polyfill indexing (patches/tiles) that trades coarse join speed for optional exact intersections, cites a Defra open‑source reader and ArcGIS GeoAnalytics Engine.
AI-written summary · about
chapters (13)
- 0:00Introduction
- 1:17Target Audience
- 3:02bottlenecks
- 3:33fundamentals
- 4:43data lake
- 5:58parallelization 101
- 7:29distribution
- 9:44geospatial vector data input
- 10:39inspire index polygons
- 10:50userdefined function
- 12:36indexing
- 17:48Azure data bricks
- 19:02Azure Geospatial Interest Group
related talks
46mGeoSpatially enabling your Big Data Apache clusters with LocationTech
23mLessons learned building a cloud native geospatial data management system
31mUnpacking the geospatial engineering toolbox – an overview of data science techniques for spatial da
21mCloud-Native Data in Practice
33mBridging Big Data and Machine Learning Ecosystems: A Cloud Native Approac...
28mA pythonista’s pipeline for large scale geospatial analytics
32mIntroduction to geospatial analysis for uninitiated SQL Data Engineer
11mTowards a Digital Society: Investigating Integration of Distributed DBs and Distributed Processing