Category: Analytics

Times Higher Education (THE) is a data-driven business that, with the help of AWS, is now realising the value of their data, which enables them to be better informed and make faster decisions for customers. THE provides a broad range of services to help set the agenda in higher education, and their insights help universities improve through performance analysis. THE worked with the AWS Data Lab to create a centralised repository of their data. Launching a data lake helped with providing a cost-effective platform and cataloguing data so they could understand their data and design new products to make use of it.

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.

Many organizations including the intelligence community, security organizations, law enforcement, regulatory bodies, news organizations, and non-governmental organizations work together to disrupt transnational crime networks. Their missions include combating illicit trade; disrupting human, animal, and narcotics trafficking; detecting money laundering; and exposing political corruption. This community needs rapid analysis of large, diverse streams of information about air transportation networks, because air transportation is the fastest way to conduct illicit trade internationally. The nonprofit Center for Advanced Defense Studies (C4ADS) built the Icarus Flights application to meet this need. By building on AWS using managed cloud services, C4ADS spends less time and energy managing infrastructure, which frees them to focus on building innovative analytics and alerting services that their user community needs.

Modern data engineering covers several key components of building a modern data lake. Most databases and data warehouses, to an extent, do not lend themselves well to a DevOps model. DataOps grew out of frustrations trying to build a scalable, reusable data pipeline in an automated fashion. DataOps was founded on applying DevOps principles on top of data lakes to help build automated solutions in a more agile manner. With DataOps, users apply principles of data processing on the data lake to curate and collect the transformed data for downstream processing. One reason that DevOps was hard on databases was because testing was hard to automate on such systems. At California State University Chancellors Office (CSUCO), we took a different approach by residing most of our logic with a programming framework that allows us to build a testable platform. Learn how to apply DataOps in ten steps.

Data lakes can help hospitals and healthcare organizations turn data into insights and maintain business continuity, while preserving patient privacy. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis. A data lake enables you to break down data silos and combine different types of analytics to gain insights and guide better business decisions. In my previous post, “Getting started with a healthcare data lake,” I shared how to get started using data lakes in managing healthcare data and what a good “first sprint” architecture might look like. Here, I walk through building your first solution on AWS using a healthcare data lake as our example workload.

Campaign contributions have grown exponentially in the United States. In 1980, there were around 500,000 contributions made; in 2020 alone, the Federal Election Commission (FEC) expects 500 million contributions. Meanwhile, the evolution of technology has changed the way Americans contribute to political campaigns, making it easier to make many small contributions. To meet unprecedented demand for data transparency, the FEC turned to the cloud.

Organizations across the globe are using advanced analytics and data science to predict and make decisions. They are finding ways to use their vast and diverse data stores to predict the best place to put their next retail store, what products to recommend to customers, how many employees they need for peak hours of operation, and how long a piece of machinery has until it needs maintenance. Public sector organizations in government, education, nonprofit, and healthcare are looking to use data to advance their missions too. Learn how.

Our cities are becoming smarter and faster every day, and as the modern city evolves so does its transportation offerings. By migrating transportation services to the cloud, cities can evolve to meet constituents’ transportation needs. To meet a modern-day travelers’ expectations, the cloud drives innovation by providing real-time analytics and predictive modeling that can make transportation easier and faster.

Prodigy Game (Prodigy) has a mission to help every child in the world love learning and make education freely available to students globally. Prodigy’s math game – geared toward learners in the first to eighth grade – allows students to hone their math skills with questions delivered according to their individual needs. As their user base grew, so did the strain on their ability to handle the increasing demands. They turned to AWS.

Oklahoma State University in Oklahoma City (OSU-OKC), a two-year, technical-focused college, has historically faced challenges with consistent reporting, database management, and analytics. Technology generalists hired to do these tasks required extensive training to successfully extract data from traditional student information systems, manipulate data for state and federal compliance reporting, and generate limited campus reporting for operational or academic program review and analysis.