Tag: data lakes

Share Our Strength, a national nonprofit organization, is dedicated to ending hunger and poverty in the US and abroad. Share Our Strength is ending childhood hunger in America by making sure all children get the healthy food they need by working with community organizations to provide funding, technical assistance, and resources across the country. Learn how Share Our Strength used AWS to overcome data management challenges and improve their strategic planning outcomes to end childhood hunger in America.

Waterford.org is an early education nonprofit committed to advancing equity and excellence for young learners. Waterford.org provides personalized programs for learners from pre-kindergarten to second grade, and support services for families and educators so the adults in a child’s life can help them reach their full potential. To deliver these educational resources to families and children, Waterford.org uses AWS.

In this guest post, Ajay K. Gupta, co-founder and chief executive officer (CEO) of HSR.health, explains how healthcare technology (HealthTech) nonprofit HSR.health uses geospatial artificial intelligence and AWS to develop solutions that support improvements in healthcare and health equity around the world.

For higher education institutions, there is a growing focus on the importance of improving the student experience, which touches on virtually everything colleges and universities do. And how can higher education institutions improve the student experience? Data. Learn how higher education institutions can use data best practices to turn their data into insight.

As one-time donations increasingly become the norm, nonprofit development teams are challenged to think outside-the-box to attract, retain, and communicate with their valuable supporters. Nonprofit organizations can use data to inform an enhanced engagement strategy, but many are challenged to unlock the full value of that data affordably and at scale. To help nonprofits use the cloud to build innovative fundraising and member engagement solutions, we are launching a new program – AWS TechAction.

Easterseals, DC MD VA is a multifaceted nonprofit organization with the goal of enriching lives and expanding opportunities for children and adults in the Washington DC, Maryland, and Virginia (DMV) area, including people with disabilities and military backgrounds. With support from our account team at AWS, Easterseals established a data lake to better understand and define the impact our organization has on its participants with the overarching goal of empowering all people to to achieve their potential and live meaningful lives.

Nonprofits are using the cloud for fundraising, donor and member management, and communications. With this move online, they have access to more data than ever. However, sharing, connecting, and interpreting data from many different sources can be a challenge. To address this challenge, today AWS and AWS Partner Salesforce.org are announcing the general availability of Data Lake for Nonprofits – Powered by AWS.

Data lakes are becoming increasingly common in many different workloads, and geospatial is no exception. In 2021, Amazon Web Services (AWS) announced geography and geohash support on Amazon Redshift, so geospatial analysts have the capability to quickly and efficiently query geohashed vector data in Amazon Simple Storage Service (Amazon S3). In this blog post, I walk through how to use geohashing with Amazon Redshift partitioning for quick and efficient geospatial data access, analysis, and transformation in your data lake.

Data lakes can help hospitals and healthcare organizations turn data into insights and maintain business continuity, while preserving patient privacy. This blog post is part of a larger series about getting started with setting up a healthcare data lake. In this blog post, I detail how the solution has evolved at a foundational level over the series to include microservices. I describe the design decisions I’ve made and the additional features used. You can access code samples for this solution through a GitHub repo for reference.

To mitigate synthetic fraud, government agencies should consider complementing their rules-based improper payment detection systems with machine learning (ML) techniques. By using ML on a large number of disparate but related data sources, including social media, agencies can formulate a more comprehensive risk score for each individual or transaction to help investigators identify improper payments efficiently. In this blog post, we provide a foundational reference architecture for an ML-powered improper payment detection solution using AWS ML services.