Spatial Algorithms at Scale with spatialpandas |SciPy 2020| Pevey, Lewis, and Pothina

How do you analyze 1 trillion rows of geospatial point data? What if you have terabytes of astronomy data? We recently faced both of these problems. We solved them by using spatialpandas, dask and terraform-jupyter to efficiently build and execute spatial algorithms at scale. Spatialpandas is a new library that provides Pandas and Dask extensions for vectorized spatial and geometric operations. Dask allowed us to scale the algorithms and terraform-jupyter gave us a cheap cloud autoscaling compute environment. In this talk we will demonstrate how you can do the same.
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Видео Spatial Algorithms at Scale with spatialpandas |SciPy 2020| Pevey, Lewis, and Pothina канала Enthought