Category: Analytics

In this post, we explore the complexities of data privacy and controls on Amazon Web Services (AWS), examine how creating a landing zone within which to contain such data is important, and highlight the differences between creating a landing zone from scratch compared with using the AWS Landing Zone Accelerator (LZA) for Healthcare. To aid explanation, we use a simple healthcare workload as an example. We also explain how LZA for Healthcare codifies HIPAA controls and AWS Security Best Practices to accelerate the creation of an environment to run protective health information workloads in AWS.

The University of Pittsburgh Athletics Department wanted to know more about its fans, so it looked for an innovative solution and turned to Amazon Web Services (AWS). By focusing on fan behavior and seeking out trends in ticket sales, the department hoped to answer questions surrounding team loyalty and how they could stay competitive. This post provides an overview of the powerful solution Pitt Athletics built to engage with and sell to its fanbase.

The Natural History Museum in London is a world-class visitor attraction and a leading science research center. NHM and Amazon Web Services (AWS) have worked together to transform and accelerate scientific research by bringing together a broad range of UK biodiversity and environmental data types in one place for the first time. In this post, the first in a two-part series, we provide an overview of the NHM-AWS project and the potential research benefits.

Amazon SageMaker is a fully managed machine learning (ML) service that provides various capabilities, including Jupyter Notebook instances. While RStudio, a popular integrated development environment (IDE) for R, is available as a managed service in Amazon Web Services (AWS) commercial Regions, it’s currently not offered in AWS GovCloud (US) Regions. Read this post, however, to learn how you can use SageMaker notebook instances with the R kernel to perform data analytics tasks in AWS GovCloud (US) Regions.

In this post, we cover an Amazon Web Services (AWS) Cloud infrastructure with a modular architecture that enables you to explore and take advantage of the benefits from different Retrieval-Augmented Generation (RAG)-based generative AI resources in a flexible way. This solution provides several benefits, along with faster time-to-market and shorter development cycles.

In this post, you will learn how to deploy Amazon Web Services (AWS) Cloud Intelligence Dashboards (CID) using the Landing Zone Accelerator on AWS (LZA) solution. In doing so, you will learn how to customize your LZA deployment using the customizations-config.yaml file. By utilizing the LZA and CID together, you can streamline the deployment process, ensure compliance with best practices, and gain valuable insights into your cloud environment, ultimately leading to improved operational efficiency, enhanced security, and better-informed decision-making.

The Amazon Web Services (AWS) Resilience Hub provides you with a single place to define your resilience goals, assess your resilience posture against those goals, and implement recommendations for improvement. In this post, we discuss how we can track the resiliency of software applications and infrastructure using AWS Resilience Hub to provide “always available” services and monitor changes to the application availability.

Kokomo24/7 is a health and safety software education technology (EdTech) platform committed to creating safer schools and communities. Kokomo used Amazon Web Services (AWS) database and analytics tools to create a health-tracking platform that allowed the Los Angeles Unified School District (LAUSD), the second-largest school district in the US, to cut its costs by 50 percent while improving flexibility and response times.

This post discusses how Amazon Web Services (AWS) can help you successfully set up an Amazon DataZone domain, aggregate data from multiple sources into a single centralized environment, and perform analytics on that data. Additionally, this post provides a sample architecture as well as a walkthrough on how to set up that architecture. Ultimately, this post serves as a valuable resource if you’re seeking to optimize your data management processes and derive actionable insights to drive business growth.

Wake Forest is a leading private university in the US with close to 9,000 enrolled students and almost 7,000 faculty and staff. With more than 82,000 degree recipients across all 50 US states and 103 foreign countries, its broader community is vast and growing. To make the most of this diverse community, university leaders want to create connections between individuals across graduating classes, disciplines, and geographies. This post highlights how the school used Amazon Web Services (AWS) to build a solution that brings its whole community closer.