A production-focused resource for implementing, optimizing, and maintaining spatial data in open table formats (Apache Iceberg, Delta Lake). The transition from monolithic spatial databases to a spatial data lakehouse is not a storage migration — it is a fundamental re-architecture of how geospatial data is serialized, versioned, indexed, and queried at scale.
This site documents the engineering contracts required to make that architecture deliver: deterministic geometry serialization (WKB / GeoParquet), partition strategies that align with real query patterns, predicate pushdown that actually pushes down, and Python orchestration that respects snapshot isolation.
Every guide is written for data engineers, platform architects, gis backend developers, and cloud/infrastructure teams. who own the pipeline end-to-end — from S3 buckets and catalog manifests to PySpark configurations and CI/CD validation gates. No vendor pitches, no toy examples; just patterns and trade-offs that survive contact with petabyte-scale data.
Pick a pillar below to explore deeper topics, troubleshooting matrices, and ready-to-paste configurations.
Decouple storage, catalog, and compute. Master geometry serialization (WKB/GeoParquet), snapshot semantics, and the Iceberg/Delta trade-offs that govern production spatial stacks.
Hierarchical grids, Z-ordering, Hilbert curves, predicate pushdown, and raster/vector hybrid layouts engineered for sub-second queries at petabyte scale.
Arrow schemas, PyIceberg, delta-rs, async catalog orchestration, and CI/CD validation — the Python contract that keeps spatial pipelines reproducible and fast.