Fetchez

Fetch geospatial data with ease.

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Fetchez is a lightweight, modular, and highly extendable Python framework designed to orchestrate geospatial data engineering workflows.

Originally developed as the core fetching engine for the CUDEM project, Fetchez has evolved into a standalone ETL platform. It seamlessly retrieves Bathymetry, Topography, Imagery, and Oceanographic data from dozens of global repositories (NOAA, USGS, Copernicus, ESA) and processes it on the fly.

❓ Why Fetchez?

Geospatial data engineering is traditionally fragmented. You often need one script to query an API, another tool to download the files, a GIS application to clip the data, and complex shell scripts to tie it all together.

Fetchez unifies the entire pipeline.

  • Unified Interface: Access 50+ different modules using the exact same syntax.

  • Parallel Fetching: High-performance, multi-threaded downloading with automatic retry, timeout handling, and partial-download resumption.

  • Infrastructure as Code: Define complex data pipelines, cropping, and gridding workflows using CLI switches or simple YAML "Recipes".

  • Pipeline Hooks: Transparently stream, filter, and process data (via globato and transformez) as it is being downloaded.

  • Execution Lifecycle: Fetchez formally separates data engineering into distinct phases (manifest -> file -> stream -> collection). This guarantees that file operations, in-memory stream processing, and final collection operations always happen in the correct order.

  • Infinite Extensibility: Built on a modern plugin architecture. Drop custom Python scripts into a local folder, or install community extensions via pip to add your own data sources, domain schemas, processing hooks, etc.

📦 Installation

pip install fetchez

Optional Extensions: To enable advanced vector boundary support (Shapefiles/GeoPackages for regions), install with the vector extras:

pip install fetchez[vector]

🐄 Quickstart

Fetch Copernicus topography and NOAA multibeam bathymetry for a specific bounding box in one command:

CLI

fetchez run -R loc:"Miami, FL" --global-hook audit copernicus multibeam

Or run a full processing pipeline from a YAML recipe:

fetchez recipes run recipes/my_dem_project.yaml

Python

import fetchez

# Fetch Electronic Nautical Chart data from NOAA
files = fetchez.get("charts", region=[-120, -118, 33, 34], hooks=['unzip', 'filename_filter:match=.000', 'audit'])

DEM Building with Globato

While Fetchez handles the data retrieval and point-streaming, its sister project Globato provides the multi_stack accumulators and multi-resolution interpolation engines needed to turn those streams into production-grade Digital Elevation Models. Check it out!

📚 Documentation

Would you like to know more? Check out our Official Documentation to learn about:

  • Modules & Bundles: Discover and learn about data fetchers.

  • The Python API: Build custom fetchers into your apps.

  • Recipes & YAML: Run custom workflows from a simple YAML configuration.

  • Hooks & Presets: Automate unzipping, filtering, and processing.

  • Domain Schemas: Enforce rigorous geospatial standards automatically.

  • Custom Plugins: Write your own data fetchers, processing hooks and extensions.

⚖ License

This project is licensed under the MIT License - see the LICENSE file for details.

Copyright (c) 2010-2026 Regents of the University of Colorado

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  • Released:
Newer release available (0.8.5)

2 maintainers

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Credits

PyPI data
Data sourced directly from PyPI's database.

Author: Matthew Love

Maintainer: Matthew Love

License

MIT License

Requires

Python >=3.12

Provides Extra

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Geospatial

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