This site exists to help analytics engineers, data platform teams, GIS backend developers, and spatial data scientists build production-grade spatial data pipelines using dbt, PostGIS, and DuckDB. It focuses on practical implementation, spatial accuracy, and pipeline reliability — not theoretical GIS concepts.
Each guide is structured around the realities of running spatial workloads inside a version-controlled, DAG-driven warehouse: enforcing CRS governance, materializing GIST indexes via post-hooks, isolating heavy spatial joins from the mart layer, and wiring topology checks into CI so coordinate drift never reaches production.
The execution boundaries that separate a brittle GIS script from a reproducible dbt pipeline. Choose the right spatial adapter, set up PostGIS or DuckDB, design dependency graphs, validate geometry, and wire spatial tests into CI.
Treat geometry, topology, and coordinate systems as first-class citizens. Set CRS governance policy, document spatial lineage, monitor feed freshness, version schemas, scope access, and handle datasets that exceed warehouse memory budgets.
Compile-time abstraction vs. runtime execution. Build reusable spatial macros, keep them portable across warehouses, optimize proximity joins, use index hints intelligently, and run incremental spatial materializations at scale.
Every page assumes you already speak SQL, Jinja, and DAGs. Code samples target PostGIS,
DuckDB Spatial, Snowflake GEOGRAPHY, and BigQuery
GEOGRAPHY — with explicit notes when behaviour diverges.