Tag: Data Lake
The first blog post in this series outlined considerations for developing API pipelines on AWS to extract data from third-party SaaS tools. With consolidated data, public sector organizations can offer new experiences for donors and members, enrich research datasets, and improve operational efficiency for their staff. This follow-up blog post presents options for ingesting SaaS data with API requests from AWS services, methods for handling payload data from API calls, and guidance for orchestration and scaling an API data pipeline on AWS.
Public sector organizations often utilize third-party Software-as-a-Service (SaaS) to manage various business functions, such as marketing and communications, payment processing, workflow automation, donor management, and more. This common SaaS landscape can lead to data silos where data becomes isolated in disparate systems and difficult to centralize for business insights. If existing SaaS connectors are not available, public sector organizations can use AWS to build an API-driven data pipeline to consolidate data from SaaS platforms offering open APIs. In this post, learn how to build an API data pipeline on AWS.
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.
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.
How advanced analytics can improve efficiency and provide important student insights at higher education institutions
Higher education institutions are turning to data to solve challenges, improve efficiency, and create a better student experience. But to obtain the answers institutions need to thrive and better serve students, they often need new technologies. Many universities are upgrading IT systems and investing in state-of-the-art analytics solutions. Case Western Reserve University (CWRU) is one such institution that has taken steps to innovate by creating opportunities for evolution with AWS.
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.
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.
Data Lake for Nonprofits – powered by AWS and Salesforce.org now available to help unleash data insights
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.