Data engineers have used AWS Glue to create, run, and monitor extract, transform, and load jobs since 2016. Now, AWS added a new visual data preparation tool that enables customers to clean and normalize data without writing code.
Over 250 built-in functions
AWS Glue DataBrew enables end-users to easily access and visually explore any amount of data across their organization directly from their Amazon Simple Storage Service (S3) data lake, Amazon Redshift data warehouse, and Amazon Aurora and Amazon Relational Database Service (RDS) databases. Customers can benefit from over 250 built-in functions to combine, pivot, and transpose the data without writing code.
Raju Gulabani, VP of Database and Analytics, AWS, said,
“Customers love the scalability and flexibility of code-based data preparation services like AWS Glue, but they could also benefit from allowing business users, data analysts, and data scientists to visually explore and experiment with data independently, without writing code. AWS Glue DataBrew features an easy-to-use visual interface that helps data analysts and data scientists of all technical levels understand, combine, clean, and transform data.”
This tool recommends data cleaning and normalization steps like filtering anomalies, normalizing data to standard date and time values, generating aggregates for analyses, and correcting invalid, misclassified, or duplicative data. In addition to this, AWS Glue DataBrew is serverless and fully managed, so customers never need to configure, provision, or manage any compute resources.
AWS Glue DataBrew is generally available today in US East (N. Virginia), US East (Ohio), US West (Oregon), EU (Ireland), EU (Frankfurt), Asia Pacific (Sydney), and Asia Pacific (Tokyo), with availability in additional regions coming soon.
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