Amazon SageMaker Data Wrangler reduces the time to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio. Data Wrangler can simplify your data preparation and feature engineering processes and help you with data selection, cleaning, exploration, and visualization. Data Wrangler has over 300 built-in transforms written in PySpark, so you can process datasets up to hundreds of gigabytes efficiently on the default instance, ml.m5.4xlarge.
However, when you work with datasets up to terabytes of data using built-in transforms, you might experience longer processing time or potential out-of-memory errors. Based on your data requirements, you can now use additional Amazon Elastic Compute Cloud (Amazon EC2) M5 instances and R5 instances. For example, you can start with a default instance (ml.m5.4xlarge) and then switch to ml.m5.24xlarge or ml.r5.24xlarge. You have the option of picking different instance types and finding the best trade-off of running cost

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