Category: AWS Glue

For the more than 2,800 administrators across approximately 1,200 participating institutions in the US, that means access to near daily provided to them through Amazon Quick Sight on Amazon Web Services (AWS) by the eduroam team at Internet2. However, this wasn’t always the case. This post walks through how the eduroam team modernized their data reporting pipeline, the architecture behind it, and the impact it’s had on institutional engagement.

Credit unions exist to serve members, not to run data centers. Every hour our team spends on infrastructure is an hour not spent on the question a member actually cares about: “Is my money safe, is my experience easy, and is my credit union on my side?” Moving to a serverless, managed-service foundation on Amazon Web Services (AWS) means our engineers spend their time on membership modeling, fraud analytics, and AI augmentation instead of capacity planning.

Learn how the GDAC supports over 35 agencies, turning siloed information into the cross-agency insights Georgia’s policymakers need to better serve constituents.

Learn how the Boise State University Foundation transformed quarterly Microsoft Excel reports into real-time dashboards using Amazon Quick, a 780-fold improvement that fundamentally changed how leadership makes decisions. It extracts actionable insights from a decades-old legacy system while supporting a major comprehensive fundraising campaign.

Learn how The Ohio State University transformed their data infrastructure by migrating to Amazon Web Services (AWS), reducing critical data processing times by 50% while creating a unified data environment.

This post shares lessons learned by Pariveda, a strategy and technology services firm, during the implementation of a scalable and flexible data integration pipeline for a customer. We review three approaches, all leveraging AWS Glue and Salesforce capabilities, to securely publish data to polymorphic objects and compounded fields.

The University of Minnesota Athletics Department had access to a lot of data, but lacked a way to bring it together. Even answering simple questions, like how many tickets were sold and who received them, required hours of cross-platform data wrangling. In less than a year, the department replaced that complexity with a scalable data lake built on AWS. The new data architecture—which gives the department full visibility into ticketing transactions and digital behavior—was built by a small internal team, without requiring a complete overhaul of their existing systems.

In this post, we use AWS Glue, Amazon Athena, and Amazon QuickSight to build a dashboard that tracks costs associated with AWS and third-party providers. This single pane of glass allows for quick monitoring at the senior leadership level, with the ability to dig further into specific spending categories for tools and environments. Additionally, this data system makes further analytics possible.

Mid-Hudson Valley Federal Credit Union (MHVFCU) needed to automate their tools and develop advanced forecasting capabilities to make informed decisions for deposit management and growth to maximize benefit for their members. For this purpose, MHVFCU leadership sought a solution that would not only save time in data preparation and analysis but also provide accurate forecasts for balances across four key deposit products: savings, checking, certificate deposits (CDs), and money market. Read this post to learn about the solution they built with AWS.

What if a Medicaid agency could make sure that their leadership and staff had access to a reliable, single source of truth for their data in one unified platform? The implementation of an operational data store (ODS) presents a viable solution. This post explains the architectural choices available with Amazon Web Services (AWS) to effectively create and maintain an ODS.