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SERIES3 OF 3 PUBLISHED

Geospatial in Cloud

A 3-part comparison of Databricks, AWS, and GCP for geospatial workloads. Real benchmarks, real code, honest trade-offs. No vendor hype - just practical decision criteria from production deployments.

3 parts
45 min total
PLATFORMS:
Databricks
AWS
GCP
1
AVAILABLE15 min

Geospatial in Cloud: Databricks

When Databricks makes sense for geospatial. Mosaic + Photon performance, spatial SQL with 90+ native functions, and the lakehouse architecture for spatial data.

  • -Mosaic + Photon performance
  • -Spatial SQL with H3 indexing
  • -When NOT to use Databricks
2
AVAILABLE15 min

Geospatial in Cloud: AWS

The AWS geospatial stack from orchestration to storage. Glue for ETL, Lambda for serverless processing, S3 + COG for cloud-native storage, and Athena for spatial SQL.

  • -Glue ETL orchestration
  • -Lambda + S3 + COG pattern
  • -Athena spatial SQL queries
3
AVAILABLE15 min

Geospatial in Cloud: GCP

Google Cloud for geospatial at scale. Dataproc for distributed processing, BigQuery GIS for massive spatial SQL, Earth Engine for satellite imagery, and Cloud Run for serverless.

  • -Dataproc distributed processing
  • -BigQuery GIS at scale
  • -Earth Engine integration
BEGIN THE SERIES

Start with Part 1:
Databricks for Geospatial

Understand when Databricks is the right choice for geospatial workloads. Then compare against AWS and GCP with real benchmarks and production code examples.

Read Part 1: DatabricksGet Workflow Assessment
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