Category: Amazon Athena

As the amount of health data increases, different healthcare, life sciences, population health, and public health organizations are working to modernize their data infrastructure, unify their data, and innovate faster with technologies like artificial intelligence and machine learning (AI/ML). In this blog post, we dive deep on architecture guidance that enables healthcare providers to improve patient care.

In 2020, Meta introduced the Daylight Map Distribution, which combines OpenStreetMap (OSM) data with quality and consistency checks from Daylight mapping partners to create a no-cost, stable, and simple-to-use global map. This blog post provides a brief overview of OSM and Daylight followed by a step-by-step tutorial using five real-world examples. We combine the powerful query capabilities of Amazon Athena from with the feature-rich Daylight OSM data to demonstrate a typical OSM data analysis workflow.

At AWS, many public sector customers operate fleets of vehicles (e.g. emergency response, public transportation) that generate location data, which is ultimately stored in a data lake. These customers frequently ask how they can quickly visualize this data and extract insights that can help them optimize how they operate their vehicle fleets. In this post, learn how to use Amazon Athena and Amazon Location Service to perform ad hoc reverse geocoding on a notional dataset of vehicle location history, and visualize the results on an Amazon QuickSight map.

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.

A research team led by Dr. Beatriz Martinez Lopez at UC Davis supports pig farmers with a data analytics platform that aggregates and analyzes animal health data to diagnose animal viruses and diseases. But this platform was primarily designed for analysts and data scientists. To truly transform animal disease management, Martinez-Lopez wants to put this data analytics tool into the hands of farmers around the world. So the research team is using the scalable, cost-effective tools of the AWS Cloud, along with a research grant letter of support from AWS, to make this optimized platform a reality.

The AWS Open Data Sponsorship Program makes high-value, cloud-optimized datasets publicly available on AWS. We work with data providers to democratize access to data by making it available to the public for analysis on AWS; to develop new cloud-native techniques, formats, and tools that lower the cost of working with data; and to encourage the development of communities that benefit from access to shared datasets. This quarter, we released 19 new or updated datasets like validated OpenStreetMap data, bioacoustic data, COVID-19 detection data, and more.

Using data lakes, nonprofits can use data to influence strategy and inform decisions that produce value and impact. In this post, learn how to build a data lake, ingest data from a PostgreSQL server, give permissions to users to consume the data using AWS Lake Formation, and access and analyze the data using Amazon Athena.

If you share data with other researchers, especially if they are using a different tool, you can quickly run into version issues, not knowing which file is the most current. Rather than sending data files everywhere, AWS offers a simple way to store your data in one central location so that you can read your data into SAS and still share it with other colleagues. In this blog post, I will explain how to export your data, store it in AWS, and query the data using SAS.