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 upload a dataset, clean and normalize the data, and train and publish an ML model. We look for anomalies by applying the Amazon SageMaker Random Cut Forest (RCF) anomaly detection algorithm on a public dataset that records power consumption for more than 300 random households. To make it easier for you to get started, we created an AWS CloudFormation template that automatically configures a Jupyter notebook instance with the required libraries and installs the plugin. We used Amazon Deep Learning AMI to configure the out-of-the-box Jupyter server.

Amazon Web Services' new visual data preparation tool for AWS Glue allows users to clean and normalize data with an interactive point-and-click visual interface without writing custom code. AWS Glue DataBrew helps data scientists and data analysts get the data ready for analytics and machine learning (ML) 80 percent quicker than traditional data preparation approaches, according to the cloud provider, which made the tool generally available on Wednesday. The new offering builds on AWS Glue, which AWS generally released in April of 2017. AWS Glue is a serverless, fully managed, extract, transform and load (ETL) service to categorize, clean, enrich and move data between various data stores. It has a central data repository called the AWS Glue Data Catalog, an ETL engine that generates Python code automatically and a flexible scheduler to handle dependency resolution, job monitoring and retries.

Inc. company announced the general availability of AWS Glue DataBrew, a new visual data preparation tool that enables customers to clean and normalize data without writing code. Since 2016, data engineers have used AWS Glue to create, run, and monitor extract, transform, and load (ETL) jobs. AWS Glue provides both code-based and visual interfaces, and has dramatically simplified extracting, orchestrating, and loading data in the cloud for customers. Data analysts and data scientists have wanted an easier way to clean and transform this data, and that's what DataBrew delivers, with a service that allows data exploration and experimentation directly from AWS data lakes, data warehouses, and databases without writing code. AWS Glue DataBrew offers customers over 250 pre-built transformations to automate data preparation tasks (e.g.