Spatial Lakehouse Architectures

A production reference, not a marketing site

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.

The catalogue now spans four sections and twenty-five topic areas, from geometry serialization and validation through partitioning, join tuning and streaming ingest to the query engines and the aggregation layer that serves maps and dashboards. Every page carries runnable code, the measurements that justify each recommendation, and the failure modes that make the recommendation necessary.

Pick a section below to explore deeper topics, troubleshooting matrices, and ready-to-paste configurations — or start with the deep-dives immediately below, which answer the questions teams ask first.