Author: Ethan Fahy

Many public sector customers in government, healthcare, and life sciences have data lakes that contain addresses (e.g., 123 Main Street). These customers frequently ask how they can quickly visualize these addresses on a geographic map to get a more intuitive understanding of how these addresses are distributed. In this post, learn how to use Amazon Athena and Amazon Location Service to perform ad hoc geocoding on an example dataset and visualize these geocoded addresses on an Amazon QuickSight map.

Geospatial datasets are increasingly large, reaching terabyte and even petabyte scale, which can cause many challenges for geospatial analysts and educators–but Amazon AppStream 2.0 can provide some solutions. In this blog post, we walk through how to deploy QGIS, a no cost, open-source geospatial information system (GIS) application used by geospatial analysts, in Amazon AppStream 2.0. We also load an example dataset to demonstrate how desktop GIS application users can access large, cloud-hosted geospatial datasets with high performance by keeping the data and compute components together on the cloud, and streaming the desktop application instead of downloading the data itself.

Terabytes of Earth Observation (EO) data are collected each day, quickly leading to petabyte-scale datasets. By bringing these datasets to the cloud, users can use the compute and analytics resources of the cloud to reliably scale with growing needs. In this post, we show you how to set up a Pangeo solution with Kubernetes, Dask, and Jupyter notebooks step-by-step on Amazon Web Services (AWS), to automatically scale cloud compute resources and parallelize workloads across multiple Dask worker nodes.