Tag: AWS Lake Formation

The Amazon Web Services (AWS) commitment to safe, transparent, and responsible artificial intelligence (AI)—including generative AI—is reflected in our endorsement of the White House Voluntary AI Commitments, our participation in the UK AI Safety Summit, and our dedication to providing customers with features that address specific challenges in this space. In this post, we explore how AWS can help agencies address the governance requirements outlined in the Office of Management and Budget (OMB) memo M-2410 as public sector entities look to build internal capacity for AI.

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.

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.

Introduction Government authorities such as tax, unemployment insurance, and other finance agencies across the US and globally are seeking ways to innovate. They are trying to unlock insights from their data, deliver better customer experiences, and improve operations using cutting-edge technologies such as generative artificial intelligence (AI), machine learning (ML), and other data analytics tools. […]

Beginning on April 1, 2023, state Medicaid agencies (SMA) will have one year to “unwind” temporary COVID-era changes and return to pre-pandemic ways of working. A major part of that will be re-verifying that all 91 million members still qualify to receive Medicaid benefits. For nearly a year, AWS has supported SMAs with in-house Medicaid expertise to identify unwinding issues and develop solutions to address them. The top four concerns that SMAs have shared are in approaching outreach and engagement, staffing shortages, returned mail, and reporting capabilities. Learn how AWS can help states across the country overcome these challenges across different scenarios.

Cybersecurity analytics is a systematic methodology designed to collect, ingest, process, aggregate, and analyze security events. This methodology empowers organizations to proactively perform security investigations, powered by advanced analytics and machine learning (ML), which help mitigate cyber issues more effectively and efficiently at scale. Learn about the core components of a cybersecurity analytics framework and how organizations can use AWS to design a cybersecurity analytics platform with analytics and ML services.

Using data lakes, nonprofits can use data to influence strategy and inform decisions that produce value and impact. In this post, learn how to build a data lake, ingest data from a PostgreSQL server, give permissions to users to consume the data using AWS Lake Formation, and access and analyze the data using Amazon Athena.

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.

Research computing has come a long way from the mainframes of the 1960s. At the recent Practice and Experience in Advanced Research Computing (PEARC) conference, I noted four emerging themes that underscore how the field continues to evolve.

If you are a scientific researcher, you are likely more interested in getting your research done than in the computational resources that you use to do it. You may think about ways to continue your research remotely with the rise in remote work. Did you know the cloud and Amazon Web Services (AWS) can accelerate your research and time to science? Here are five ways.