A focused playbook for engineering teams running spatial data at scale. Migrate legacy Shapefiles and GeoJSON to columnar, cloud-native formats like GeoParquet and FlatGeobuf, and tune the storage layer for predictable cost and query latency.
Practical guides for GIS data engineers, Python backend developers, and cloud architects — covering ZSTD level selection, row-group sizing, quadtree partitioning, schema mapping, metadata preservation, and CI/CD validation for resilient migration pipelines.
Three connected tracks. Start with format trade-offs, then layer compression and indexing on top, and finally automate the migration end-to-end with production-grade pipelines.
Modern geospatial storage architecture: WKB, CRS handling, columnar vs streaming formats, and the trade-offs between GeoParquet, FlatGeobuf, GeoJSON, and Shapefile.
Tune ZSTD levels, size row groups, apply dictionary encoding, and partition with quadtree / Hilbert curves so spatial queries stay fast at scale.
Batch convert legacy Shapefile / GeoJSON to GeoParquet with Python: schema mapping, metadata preservation, parallel execution, validation, and fallback routing.
New to cloud-native geospatial storage? These are the most-referenced guides — one strong entry point into each track. Read them in order to go from format choice to a tuned, automated migration.
Pick the right cloud-native format: columnar GeoParquet for analytics vs streaming FlatGeobuf for feature-by-feature reads.
Choose a ZSTD level that balances file size, write cost, and query latency for vector and raster workloads.
Automate Shapefile and GeoJSON migration to GeoParquet with schema mapping, parallel execution, and validation.