AWS Big Data Blog

Tag: Data Lake

A public data lake for analysis of COVID-19 data

As the COVID-19 pandemic continues to threaten and take lives around the world, we must work together across organizations and scientific disciplines to fight this disease. Innumerable healthcare workers, medical researchers, scientists, and public health officials are already on the front lines caring for patients, searching for therapies, educating the public, and helping to set […]

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How Siemens built a fully managed scheduling mechanism for updates on Amazon S3 data lakes

Siemens is a global technology leader with more than 370,000 employees and 170 years of experience. To protect Siemens from cybercrime, the Siemens Cyber Defense Center (CDC) continuously monitors Siemens’ networks and assets. To handle the resulting enormous data load, the CDC built a next-generation threat detection and analysis platform called ARGOS. ARGOS is a […]

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ETL and ELT design patterns for lake house architecture using Amazon Redshift: Part 2

Part 1 of this multi-post series, ETL and ELT design patterns for lake house architecture using Amazon Redshift: Part 1, discussed common customer use cases and design best practices for building ELT and ETL data processing pipelines for data lake architecture using Amazon Redshift Spectrum, Concurrency Scaling, and recent support for data lake export. This […]

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ETL and ELT design patterns for lake house architecture using Amazon Redshift: Part 1

Part 1 of this multi-post series discusses design best practices for building scalable ETL (extract, transform, load) and ELT (extract, load, transform) data processing pipelines using both primary and short-lived Amazon Redshift clusters. You also learn about related use cases for some key Amazon Redshift features such as Amazon Redshift Spectrum, Concurrency Scaling, and recent […]

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Matching patient records with the AWS Lake Formation FindMatches transform

Patient matching is a major obstacle in achieving healthcare interoperability. Mismatched patient records and inability to retrieve patient history can cause significant barriers to informed clinical decision-making and result in missed diagnoses or delayed treatments. Additionally, healthcare providers often invest in patient data deduplication, especially when the number of patient records is growing rapidly in […]

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Provisioning the Intuit Data Lake with Amazon EMR, Amazon SageMaker, and AWS Service Catalog

This post outlines the approach taken by Intuit, though it is important to remember that there are many ways to build a data lake (for example, AWS Lake Formation). We’ll cover the technologies and processes involved in creating the Intuit Data Lake at a high level, including the overall structure and the automation used in provisioning accounts and resources. Watch this space in the future for more detailed blog posts on specific aspects of the system, from the other teams and engineers who worked together to build the Intuit Data Lake.

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Access and manage data from multiple accounts from a central AWS Lake Formation account

his post shows how to access and manage data in multiple accounts from a central AWS Lake Formation account. The walkthrough demonstrates a centralized catalog residing in the master Lake Formation account, with data residing in the different accounts. The post shows how to grant access permissions from the Lake Formation service to read, write and update the catalog and access data in different accounts.

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Integrate and deduplicate datasets using AWS Lake Formation FindMatches

AWS Lake Formation FindMatches is a new machine learning (ML) transform that enables you to match records across different datasets as well as identify and remove duplicate records, with little to no human intervention. FindMatches is part of Lake Formation, a new AWS service that helps you build a secure data lake in a few simple steps.

To use FindMatches, you don’t have to write code or know how ML works. Your data doesn’t have to include a unique identifier, nor must fields match exactly.

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