Cloud-native formats
COG, Zarr, GeoParquet and STAC: reading the part you need over the network instead of downloading the file.
Everything on Spatial Workflow about cloud-native formats, in the order that usually works: understand the idea, then run the task, then fix it when it goes wrong. 15 guides in total.
2 learning paths run through this topic — follow one if you would rather work through a route than pick a single guide.
🧭 Start with the concepts
The ideas the how-to guides below assume you already have.
- Cloud-Native Geospatial Explained: Why the Format Became the API cloud native geospatial explained
- Cloud-Optimised GeoTIFF Explained: Tiles, Overviews and Range Requests cloud optimised geotiff explained
- Lazy Loading Explained: Why xarray Reads Nothing Until You Ask lazy loading xarray explained
- Chunked Arrays and Zarr Explained zarr chunked arrays explained
⚙️ Then do the task
Step-by-step walkthroughs with code you can copy and adapt.
- How to Read a COG from a URL Without Downloading the Whole File read cog from url python
- How to Write a Cloud-Optimised GeoTIFF in Python write cloud optimised geotiff python
- How to Read Spatial Data from S3 and Other Object Storage read spatial data from s3 python
- How to Turn a STAC Search into an xarray Data Cube stac to xarray cube python
- How to Choose Chunk and Tile Sizes That Actually Help choose chunk and tile sizes
- How to Write Partitioned GeoParquet and Query It with Filters partitioned geoparquet python
- How to Scale a GeoPandas Job with dask-geopandas dask geopandas scale python
- How to Render Raster Tiles from a COG in Python raster tiles from cog
🔧 When it breaks
The errors this topic produces, with the cause explained rather than just the command.
Learning paths through this topic
A curated route rather than a list — the guides in the order that builds on itself.
Related topics
Subjects that keep coming up alongside this one, most overlap first.