Chapter 9: Geospatial Data Engineering

Geospatial data engineering turns messy spatial inputs into reliable data products. It includes ingestion, validation, transformation, reprojection, tiling, cataloging, publication, monitoring, and governance.

Learning Goals

• Design reproducible spatial data pipelines.
• Validate geometry, CRS, schema, lineage, and quality.
• Choose appropriate storage and processing patterns.
• Build batch, streaming, and cloud-native geospatial workflows.

Theory

Spatial pipelines must preserve meaning as data moves. A pipeline that clips, reprojects, simplifies, joins, or rasterizes data changes it. These changes should be explicit, tested, and recorded.

Modern geospatial data engineering favors immutable source data, versioned derived products, metadata catalogs, cloud-native formats, and automated checks.

Math

Important math includes reprojection, resampling, raster algebra, simplification, tiling, aggregation, interpolation, and statistical quality checks. Every transformation can introduce error or change resolution.

Equation companion: Math and Algorithms Reference

Tools of the Trade

• GDAL/OGR, ogr2ogr, gdalwarp.
• Airflow, Dagster, Prefect.
• dbt, Great Expectations.
• Spark, Dask, DuckDB, GeoPandas.
• STAC, COG, GeoParquet, Zarr.
• Object storage, containers, queues, orchestration.

Examples of Real-World Solutions

• A nightly parcel update pipeline validates geometry and publishes vector tiles.
• A satellite pipeline converts scenes into cloud-optimized analysis-ready products.
• A mobility platform consumes GPS streams and writes cleaned trajectories.
• A data portal publishes cataloged datasets with lineage and license metadata.

Working Practice Examples

1. Build an ETL pipeline that downloads public boundaries, validates them, reprojects them, and writes GeoPackage and GeoParquet outputs.
2. Add checks for invalid geometry, missing CRS, stale timestamps, and duplicate IDs.
3. Create STAC-like metadata for a raster collection.
4. Compare cost and performance for local, warehouse, and distributed processing.

Common Failure Modes

• Silent CRS changes.
• Manual edits outside the pipeline.
• No source data retention.
• No quality thresholds.
• Treating metadata as optional.

Works Cited

"GDAL Documentation." GDAL, https://gdal.org/. Accessed 9 May 2026.

"SpatioTemporal Asset Catalog Specification." STAC, https://stacspec.org/. Accessed 9 May 2026.

"Cloud Optimized GeoTIFF." Cloud Optimized GeoTIFF, https://www.cogeo.org/. Accessed 9 May 2026.

"GeoParquet Specification." GeoParquet, https://geoparquet.org/. Accessed 9 May 2026.

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