Tag: AWS Lake Formation

In an earlier post, Build secure and scalable data platforms for the European Health Data Space (EHDS) with AWS, we discussed a reference architecture for building secure and scalable data platforms for secondary usage of health data in alignment with the European Health Data Space (EHDS) using Amazon Web Services (AWS). This follow-up post walks you through the technical implementation details for building such federated data governance and analysis platforms using AWS. Whether you are a healthcare organization, technology provider, or systems integrator, this post aims to equip you with the technical knowledge to build a secure data platform for secondary usage of health data in alignment with the EHDS Regulation.

Because more than two-thirds of service members who die by suicide have no history of mental illness or suicidal ideation, Stop Soldier Suicide (SSS) started the Black Box Project in partnership with Amazon Web Services (AWS) Professional Services. Launched as an early prototype to identify data from devices of those lost to suicide, the project is an effort to gain better insight into the warning signs of suicide in veterans to help support suicide postvention, intervention, and ultimately prevention. Read this post to learn more.

In this post, we explore how healthcare and life sciences organizations can embrace the data mesh and data as a product (DaaP) principles to unlock the full potential of their health data, drive faster and more efficient drug development, and ultimately, bring life-saving treatments to patients more quickly. We also showcase the Amazon Web Services (AWS) services that support the journey towards effective data management and alignment with data mesh principles.

Cognitive Space is a leading Amazon Web Services (AWS) Partner delivering intelligent automation to satellite constellation operations using the CNTIENT platform. The system uses AWS-powered artificial intelligence (AI) decision making to handle highly complex and dynamic satellite tasking requirements, and demanding mission requirements. This blog post provides technical guidance for building and operating mission operation centers (MOCs) on AWS.

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.