Category: Artificial Intelligence

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. MEDHOST has been providing products and services to healthcare facilities of all types and sizes for over 35 years. Today, more than 1,000 healthcare facilities are partnering with MEDHOST and enhancing their […]

In this post, we discuss how to use AWS Glue Data Catalog to simplify the process for adding data descriptions and allow data analysts to access, search, and discover this cataloged metadata with BI tools. In this solution, we use AWS Glue Data Catalog, to break the silos between cross-functional data producer teams, sometimes also known […]

You can build data lakes with millions of objects on Amazon Simple Storage Service (Amazon S3) and use AWS native analytics and machine learning (ML) services to process, analyze, and extract business insights. You can use a combination of our purpose-built databases and analytics services like Amazon EMR, Amazon OpenSearch Service, and Amazon Redshift as […]

This is a guest post by Ori Nakar, Principal Engineer at Imperva. In their own words, “Imperva is a large cyber security company and an AWS Partner Network (APN) Advanced Technology Partner, who protects web applications and data assets. Imperva protects over 6,200 enterprises worldwide and many of them use Imperva Web Application Firewall (WAF) […]

June 2024: This post was reviewed and updated to add instructions for using PyDeequ with Amazon SageMaker Notebook, SageMaker Studio, EMR, and updated the examples against a new dataset. March 2023: You can now use AWS Glue Data Quality to measure and manage the quality of your data. AWS Glue Data Quality is built on Deequ […]

Machine learning (ML) is becoming more mainstream, but even with the increasing adoption, it’s still in its infancy. For ML to have the broad impact that we think it can have, it has to get easier to do and easier to apply. We launched Amazon SageMaker in 2017 to remove the challenges from each stage […]

AWS Glue DataBrew is a new visual data preparation tool that makes it easy for data analysts and data scientists to clean and normalize data to prepare it for analytics and machine learning (ML). In this post, we examine a sample ML use case and show how to use DataBrew and a Jupyter notebook to […]

April 2024: This post was reviewed for accuracy. Developing extract, transform, and load (ETL) data pipelines is one of the most time-consuming steps to keep data lakes, data warehouses, and databases up to date and ready to provide business insights. You can categorize these pipelines into distributed and non-distributed, and the choice of one or […]

This post demonstrates how you can automate the data extraction, transformation, and use of Forecast for the use case of a retailer that requires recurring replenishment of inventory. You achieve this by using AWS Lake Formation to build a secure data lake and ingest data into it, orchestrate the data transformation using an AWS Glue workflow, and visualize the forecast results in Amazon QuickSight.

This post walks you through accessing the AWS COVID-19 data lake through the AWS Glue Data Catalog via Amazon SageMaker or Jupyter and using the open-source AWS Data Wrangler library. AWS Data Wrangler is an open-source Python package that extends the power of Pandas library to AWS and connects DataFrames and AWS data-related services (such as Amazon Redshift, Amazon S3, AWS Glue, Amazon Athena, and Amazon EMR). For more information about what you can build by using this data lake, see the associated public Jupyter notebook on GitHub.