Python Geospatial ETL &
Data Pipeline Automation

A production-focused resource for GIS analysts, data engineers, and Python developers building reliable spatial data pipelines — from raw ingestion to analysis-ready outputs.

Geospatial Data Ingestion Vector & Raster Cleaning Pipeline Orchestration
Spatial ETL Pipeline Stages Four-stage pipeline diagram showing Ingest, Clean, Validate, and Load as boxes connected by arrows. Ingest OSM · STAC · APIs Clean Geometry · CRS · Schema Validate QA gates · Topology Load PostGIS · GeoParquet

Geospatial data rarely arrives in a production-ready state. Shapefiles carry mismatched projections, satellite archives expose inconsistent band layouts, and government portals deliver data in dozens of fragmented formats. This site documents the patterns, code, and reasoning needed to build automated, fault-tolerant spatial ETL pipelines in Python — at any scale.

Every guide is written for practitioners: real production code using geopandas, rasterio, shapely, pyproj, pystac-client, and modern orchestration frameworks. Whether you are extracting OSM features via Overpass, aligning multi-source raster grids, or standardising column schemas across hundreds of shapefiles — you will find reproducible, auditable workflows here.

Guides range from architectural overviews and tool-choice decision guides to deep-dive articles focused on the exact failure modes and edge cases you will encounter in production.

Topic Sections

Section 1

Mastering Geospatial Data Ingestion in Python

Architect resilient ingestion pipelines for OSM, STAC satellite catalogs, government portals, GeoJSON APIs, and ArcGIS REST services. Covers pagination, rate limiting, CRS harmonisation, and fault-tolerant HTTP clients.

6 guides → Section 2

Automated Vector & Raster Cleaning Workflows

Eliminate projection drift, repair invalid geometries, deduplicate spatial features, align raster grids, enforce schema contracts, and pick the right output format — as idempotent pipeline stages using Shapely, GeoPandas, and Rasterio.

7 guides → Section 3

Orchestrating Spatial ETL Pipelines

Turn extract, clean, and load scripts into scheduled, restartable data products with Airflow, Prefect, and Dagster — idempotent tasks, tile and scene partitioning, backfills, incremental loads, and choosing a load target.

6 guides →

Start Here

New to spatial ETL in Python? Work through these pages in order to build a solid foundation.

Featured Guides

Fetching OSM Data via Overpass API

Production-ready Overpass QL queries, bounding box scoping, streaming XML parsers, and pipeline integration for OpenStreetMap feature extraction.

Handling Rate Limits When Downloading OSM Data

Exponential backoff with jitter, Retry-After header parsing, disk caching, and fallback architecture for sustained Overpass workflows.

Fixing Self-Intersecting Polygons in GeoPandas

Version-aware geometry repair using make_valid() and buffer(0), targeted batch application, and audit logging for production pipelines.

Syncing STAC Catalogs with pystac-client

Query temporal ranges and spatial footprints across STAC collections, implement asset-level parallel downloads, and handle pagination at continental scale.

CRS Normalization Across Mixed Datasets

Deterministic CRS resolution with pyproj, datum-shift validation, and automated projection pipelines that prevent sub-meter spatial drift.

Bulk Downloading Satellite Imagery

Chunked tiling strategies, Cloud-Optimized GeoTIFF workflows, resumable HTTP downloads, and integration with USGS EarthExplorer and similar archives.

Attribute Mapping & Schema Harmonization

Column-name normalisation across heterogeneous shapefiles, type coercion strategies, and dead-letter patterns for non-conforming attributes.

Raster Alignment & Resampling Techniques

Affine-transform alignment, GDAL warp pipelines, and band-level resampling to a unified grid — essential for multi-source composite raster workflows.

Parsing GeoJSON & Shapefile APIs

Streaming GeoJSON parsers, Shapefile chunking, authentication token lifecycle management for ArcGIS REST, and bounding-box extraction patterns.

Building Airflow DAGs for Spatial ETL

Task design across the five ETL stages, retries scoped to extraction, sensors for new STAC items, and pools that respect rate-limited spatial APIs.

PostGIS vs DuckDB Spatial for Analytical Loads

Choosing the load target that shapes your pipeline: transactional upserts and GiST indexes versus columnar scan speed over GeoParquet.

Choosing a Vector Output Format

GeoParquet vs FlatGeobuf vs Shapefile for pipeline outputs — schema limits, columnar analytics, spatial indexing, and cloud-native range reads.