Introducing AWS Glue DataBrew
AWS Glue DataBrew is a visual data preparation tool that makes it easier for data analysts and data scientists to clean and normalize data to prepare it for analytics and machine learning (ML). You can choose from over 250 prebuilt transformations to automate data preparation tasks, all without the need to write any code. You can automate filtering anomalies, converting data to standard formats and correcting invalid values, and other tasks. After your data is ready, you can immediately use it for analytics and ML projects. You only pay for what you use—no upfront commitment.
Capabilities
Profile
Evaluate the quality of your data by profiling it to understand data patterns and detect anomalies; connect data directly from your data lake, data warehouses, and databases.
Clean and normalize
Choose from over 250 built-in transformations to visualize, clean, and normalize your data with an interactive point-and-click visual interface.
Map data lineage
Visually map the lineage of your data to understand the various data sources and transformation steps that the data has been through.
Automate
Automate data cleaning and normalization tasks by applying saved transformations directly to new data as it comes into your source system.
"AWS Glue DataBrew provides a visual interface that enables both our technical and nontechnical users to analyze data quickly and easily. Its advanced data profiling capability helps us better understand our data and monitor the data quality.”
Takashi Ito
General Manager of Marketing Platform Planning Department, NTT DOCOMO
“AWS Glue DataBrew will allow our data analysts to visually inspect large datasets, clean and enrich data, and perform advanced transformations. It will empower our analysts and data scientists to perform advanced data engineering activities, giving them the freedom to explore their data while decreasing the time to derive new insights.”
Tanner Gonzalez
Analytics and Cloud leader, INVISTA
“AWS Glue DataBrew has sophisticated data profiling capabilities and a rich set of built-in transformations. This will enable our data engineers to easily explore new datasets in a visual interface, and allow analysts to shape the data for their analytics solutions.”
John Maio
Director, Data & Analytics Platforms Architecture, bp