Serverless Geospatial Processing, done right.
Authoritative engineering references for designing, deploying, and operating serverless raster and vector workflows. Built for cloud GIS engineers, Python backend devs, and platform architects who care about constraint-aware architecture, cost, and observability.
What this site is
Serverless platforms reshape how spatial data is ingested, transformed, and served. Functions replace fleets, queues replace cron, and chunked I/O replaces monolithic loads — but only if the architecture respects platform limits, package size budgets, and IAM boundaries.
Each reference here distills production patterns: event-driven shapefile ingestion, raster cold-start mitigation, dead-letter routing for vector jobs, packaging GDAL into Lambda layers, and least-privilege IAM across AWS, GCP, and Azure.
The focus throughout is constraint-aware: memory, ephemeral disk, timeout, concurrency, and cost. Examples lean on Python, GDAL, and the major-cloud event primitives, and emphasize observability and deployment automation over toy demos.
Explore the references
Event-Driven Patterns
Object-storage triggers, queue routing, batch-vs-stream, chunked I/O, and durable orchestration for spatial pipelines.
Read the guideArchitecture & Platform Limits
Memory and CPU sizing, ephemeral storage, GDAL cold-starts, and least-privilege IAM across AWS, GCP, and Azure.
Read the guidePackaging & Dependencies
Python layers, native compilation, Docker optimization, and CI/CD synchronization for geospatial dependencies.
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