Amazon SageMaker Data Wrangler reduces the time that it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. 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. You can import data from multiple data sources such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Snowflake, and 26 federated query data sources supported by Amazon Athena.
Starting today, when importing data from Athena data sources, you can configure the S3 query output location and data retention period to import data in Data Wrangler to control where and how long Athena stores the intermediary data. In this post, we walk you through this new feature.
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