Architecture & Fundamentals
The control-plane / compute / storage layering for spatial workflows. Dependency isolation for GDAL stacks, state and lineage tracking, security boundaries, and how Prefect and Dagster differ when the data is geometry.
Geospatial Orchestration Hub
Production-grade orchestration
for geospatial data pipelines.
A working reference for GIS data engineers, Python platform builders, and automation architects who treat spatial workflows as distributed systems — not as cron-driven scripts.
Design reliable DAGs for spatial ETL and ELT. Handle CRS validation, large raster and vector I/O, and the memory limits that come with them. Implement production-grade retry and backoff, route failures cleanly, and ship workflows with the same CI/CD discipline you would demand of any other service.
Every guide below is grounded in real orchestration primitives from Prefect and Dagster, annotated with the spatial constraints that quietly break naïve pipelines in production.
What you’ll find here
Three deeply connected pillars, each backed by a set of focused, production-tested guides. Start with the architecture pillar if you’re building a new platform; jump straight to Resilience if you’re fighting fires on an existing one.
Spatial Task Design & Dependency Mapping
How to decompose geoprocessing into atomic tasks with explicit data contracts. Async patterns for heavy raster work, conditional branching driven by spatial predicates, and validated sync points between transforms.
Resilience & Failure Handling
Domain-aware fault tolerance: exponential backoff tuned to OGC services, circuit breakers for flaky WMS endpoints, idempotency keys for spatial ETL, and dead-letter queues for failed geotasks.