Distributed Raster Processing

Purpose

Explain the systems concerns behind processing large raster collections across many workers without losing correctness or operational control.

Outline

• Decomposing raster workloads by scene, tile, chunk, band, and time window
• Scheduler, worker, and storage patterns for parallel geospatial computation
• Avoiding bottlenecks in reads, writes, serialization, and metadata coordination
• Handling nodata, reprojection, edge effects, and reductions across partition boundaries
• Operational concerns for retries, idempotency, observability, and cost management

Later Examples

• Comparing tile-based and scene-based processing plans
• Designing idempotent raster jobs for retry-safe execution
• Diagnosing slow distributed reads from cloud-hosted imagery