Popular geospatial libraries integrate tightly with Dask, allowing you to scale workloads to Earth-sized datasets. Coiled makes it easy to launch clusters next to that data, providing fast access to both public and your own private datasets in the cloud.
Fundamental packages like Xarray and GeoPandas integrate natively with Dask to scale. You can use the same interface for working with a 1GB file locally as for a 1TB dataset distributed across a cluster.
A rich ecosystem of geospatial tools are available, thanks to a large community of academic, industry, and government users.
Dask’s flexibility enables advanced spatial workflows that would be impractical to run at scale with traditional GISs or databases.
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Submit arbitrary functions for computation in a parallelized, eager, and non-blocking way.
Dask Bag implements operations like map, filter, groupby and aggregations on collections of Python objects.
What if you don’t have an array or dataframe? Instead of having blocks where the function is applied to each block, you can decorate functions with @delayed and have the functions themselves be lazy.
Interactively visualize a billion points with Dask and Datashader.
Load satellite imagery, mask clouds, composite across time, and export to the Internet’s favorite format.
Analyzing 24 years of variability in global sea level across space and time.
Aggregating 24 million individual tree measurements with dask-geopandas.
What if you don’t have an array or dataframe? Instead of having blocks where the function is applied to each block, you can decorate functions with @delayed and have the functions themselves be lazy.
What if you don’t have an array or dataframe? Instead of having blocks where the function is applied to each block, you can decorate functions with @delayed and have the functions themselves be lazy.
What if you don’t have an array or dataframe? Instead of having blocks where the function is applied to each block, you can decorate functions with @delayed and have the functions themselves be lazy.
