Category: Technical How-to

Health information is generally disorganized, unstructured, and stored in various formats, making it impractical to trace patient history and promote the exchange of information on a national scale with other health professionals. A solution to this issue is the creation of a unique patient registry, which can be defined as a repository of retrospective, current, and prospective information of the patient in digital format. The main objective of this registry is to promote integrated, continuous, efficient, quality health care. In addition, the registry needs to be accessible and available to different health institutions. When building these registries, it’s necessary to use standards to define not only how the information is structured, but also how it can be retrieved and shared among the different HIS in a safe, scalable, cost-efficient way. This can be done using blockchain.

Nobody wants to lose data—and setting a Recovery Point Objective (RPO) to zero makes this intent clear. Customers with government mission-critical systems often need to meet this requirement, since any amount of data loss will cause harm. RPO covers both resilience and disaster recovery—everything from the loss of an individual physical disk to an entire data center. Existing systems support RPO zero through a combination of architecture patterns (including resilient messaging) and on-premises legacy databases. Frequently interpreted as a database or storage requirement, providing for RPO zero requires thinking about the entire system. To do so, you can use AWS services and architecture patterns, which provide resilience to failure with clustering, auto scaling, and failover across multiple data centers within one region.

If you are hosting an ArcGIS web app today, then you are probably hosting it on a Windows or Linux server using traditional web server software like IIS or Apache. With the web hosting capability of Amazon S3 you can remove the need to run these servers and the maintenance, management, and monitoring overhead that comes with it. Serverless services like Amazon S3 can scale automatically and can be as simple as copying over your website assets to get up and running in minutes. This blog focuses on web app implementations using ArcGIS API for JavaScript (as other ArcGIS web apps have additional considerations).

Many government customers use AWS GovCloud (US) because it provides an environment for sensitive data and regulated workloads by addressing a number of U.S. government security and compliance requirements. In many cases, customers have a number of AWS GovCloud (US) accounts and managing authentication and authorization can require a lot of work. These customers may also use Microsoft Azure Active Directory (Azure AD) for identity management, single sign-on (SSO), and multi-factor authentication (MFA). This post builds on features and functionality announced earlier by demonstrating the necessary steps to configure Azure AD, AWS SSO, and the AWS GovCloud (US)-specific identity provider centrally for ease of management.

To help support DIL environments, Amazon Web Services (AWS) created the Snow family of products to include the AWS Snowcone and AWS Snowball devices. The Snow family moves data processing and analysis as close as necessary to where data is created in order to deliver intelligent, real-time responsiveness and streamline the amount of data transferred. To address the challenges of edge of the edge computing, we use the Snowball Edge as a central management hub and a Snowcone as an outer edge device. This how-to shows how to use Rancher as a centralized Kubernetes management tool installed on a Snowball, which has been set up to manage a single-node Kubernetes cluster on a Snowcone. This configuration allows us to fully manage the containers running on one or more Snowcones from the Snowball itself.

Serverless applications are typically discreet pieces of code that customers can use to manage security-related processes or stitch together multiple AWS services to solve a larger problem. They allow customers to build and run applications and services without dealing with infrastructure management tasks such as server or cluster provisioning, patching, operating system maintenance, and capacity provisioning. In this blog, I explain the serverless computing model, the Serverless Application Repository (SAR), solution constructs and implementations, why they matter to our government customers, and how they can use them to solve common problems.

Now more than ever, citizens expect effective communications from government agencies in response to COVID-19. These state and local leaders are committed to serving their citizens with the latest news as fast as possible, but not all strategies reach citizens in real time. However, it takes time for the government to implement widely available communication services to provide timely, accurate information. One solution is to concentrate the delivery of information in a single communication channel: SMS text messages.

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.

Artificial intelligence (AI) solutions are helping solve some of the biggest short- and long-term challenges within the public sector. But how can you quickly identify new AI-powered opportunities and use cases to solve unique and specific challenges within your organization? AWS AI and ML Week, taking place from September 14-18, includes seven curated webinars from introductory to expert level. Whether you’re new to AI or an experienced machine learning developer, attend our webinars and dive into some of the most common AI use cases for the public sector from this year.

Research is done differently in the cloud than in an on-premises lab. Research labs looking to move computational research to the cloud should start with their workflows. There are common themes across computational research workflows that researchers should consider as they begin to move their research workflows to AWS.