GeoSpatially enabling your Big Data Apache clusters with LocationTech
summary
Shows how Apache stacks (Spark, Accumulo/HBase/Cassandra) get geospatial capabilities via LocationTech projects GeoMesa, GeoWave and GeoTrellis, using space-filling curves (Hilbert) to turn 2‑D/3‑D queries into fast 1‑D range scans. Demonstrates ingest and batch Spark jobs that processed Landsat 8 imagery at scale (over 2 petabytes, >1M scenes on S3), dynamic per‑request servers for zonal summaries and a climate model demo that highlights large temperature differences (example: Iowa ~+20°F).
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chapters (27)
- 0:00Introduction
- 1:12Processing geospatial data at scale
- 3:55What is processing geospatial data
- 7:57geospatial data at scale
- 10:37processing data scale
- 11:12Apache Spark
- 20:27Spatial Curves
- 26:55GeoTroll
- 28:40Process vs User
- 29:35Processing Pipeline
- 29:59Dynamic Pipeline
- 30:44Less Flexibility
- 31:24Static vs Dynamic
- 31:43Web servers
- 31:55In ingest
- 32:31Elevation
- 32:52Spark
- 33:44Pyramid
- 33:55Map
- 34:28Blue Ridge Mountains
- 35:04Flood Modeling
- 35:57Climate Forecasting
- 37:01Open Source Libraries
- 37:29Raster Data
- 37:39Python bindings
- 38:12Geo Docker
- 38:28Get involved
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