What is AWS Glue DataBrew? - AWS Glue DataBrew

What is AWS Glue DataBrew?

AWS Glue DataBrew is a visual data preparation tool that enables users to clean and normalize data without writing any code. Using DataBrew helps reduce the time it takes to prepare data for analytics and machine learning (ML) by up to 80 percent, compared to custom developed data preparation. You can choose from over 250 ready-made transformations to automate data preparation tasks, such as filtering anomalies, converting data to standard formats, and correcting invalid values.

Using DataBrew, business analysts, data scientists, and data engineers can more easily collaborate to get insights from raw data. Because DataBrew is serverless, no matter what your technical level, you can explore and transform terabytes of raw data without needing to create clusters or manage any infrastructure.

With the intuitive DataBrew interface, you can interactively discover, visualize, clean, and transform raw data. DataBrew makes smart suggestions to help you identify data quality issues that can be difficult to find and time-consuming to fix. With DataBrew preparing your data, you can use your time to act on the results and iterate more quickly. You can save transformation as steps in a recipe, which you can update or reuse later with other datasets, and deploy on a continuing basis.

The following image shows how DataBrew works at a high level.

A simple diagram about how DataBrew works. DataBrew can visually clean, prepare, and transform data without the need to write code. A box shows data entering DataBrew from Amazon S3. It shows boxes for a few of the transforms that DataBrew can do. The transform boxes include the following: Format, clean and standardize data. Restructure and transform data. Handle missing and invalid data. Handle categorical variables. Handle numerical variables. use natural language processing. The diagram shows that the data is exported to S3 as a prepared dataset.

To use DataBrew, you create a project and connect to your data. In the project workspace, you see your data displayed in a grid-like visual interface. Here, you can explore the data and see value distributions and charts to understand its profile.

To prepare the data, you can choose from more than 250 point-and-click transformations. These include removing nulls, replacing missing values, fixing schema inconsistencies, creating columns based on functions, and many more. You can also use transformations to apply natural language processing (NLP) techniques to split sentences into phrases. Immediate previews show a portion of your data before and after transformation, so you can modify your recipe before applying it to the entire dataset.

After DataBrew has run your recipe on your dataset, the output is stored in Amazon Simple Storage Service (Amazon S3). After your cleansed, prepared dataset is in Amazon S3, another of your data storage or data management systems can ingest it.