Today, we are excited to announce the ability to dynamically support different datasets stored on S3 through use of parameters in Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. Previously, customers did not have an easy way to dynamically refer to data sets when running Data Wrangler processing jobs on a schedule. Customers also lacked a way to more easily filter down files in an S3 bucket to be used for processing. Finally, customers lacked a simple way to change data sources when running a Data Wrangler processing job from the Create Job workflow or from a Data Wrangler processing notebook.