Tag: Data Lake

In this blog post series on getting started building a data lake for healthcare with AWS, I focus on improving the security posture of our example build by incorporating the relatively new Attribute Based Access Control (ABAC) feature of Amazon Cognito. This helps to both streamline and improve the granularity of access control for various user profiles connected to our data lake scenario.

Earlier this year, the Australian Bureau of Statistics (ABS) ran the Australian Census, the agency’s most significant workload, on Amazon Web Services (AWS). The Census is the most comprehensive snapshot of the country, and includes around 10 million households and over 25 million people. With the COVID-19 pandemic causing lockdowns across the country, ABS needed a digital option for the Census that was accessible and reliable for millions of people. They turned to the cloud.

For more than 150 years, Children’s National Hospital has worked to bring health and well-being to children around the world. Today, it is among the nation’s top 10 children’s hospitals and is transforming pediatric medicine for all children. Recently, the hospital opened the Children’s National Research & Innovation Campus (CNRIC). Amazon Web Services (AWS) is pleased to help launch this one-of-a-kind hub for pediatric medical discovery, innovation, and care.

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.

By taking the lead in digital healthcare transformation, Wellforce is estimated to save as much as 20 percent annually (approximately $3 million USD) through the modernization of the healthcare IT ecosystem using the cloud. This innovative approach serves as one of the first examples that healthcare systems across the nation, and world, can replicate.

Nonprofits and education institutions of all sizes rely on large amounts of data to serve their stakeholders, programs, and governance. For many organizations, the first step in a technology transformation begins with centralizing data that is siloed across a variety of mission critical systems. In support of these goals, Salesforce.org and Amazon Web Service (AWS) are working together to help nonprofits and education institutions derive actionable insights from their data.

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.

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.

According to the National Center for Education Statistics, only 60 percent of college students receive a degree within six years. Universities—like Portland State University (PSU) and Oklahoma State University (OSU-OKC)—are using data lakes for analytics and machine learning to improve academic achievement by helping students reach their educational goals faster. Read on for how institutions use Amazon S3 for data lakes.

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.