If you use the default lifecycle configuration for your domain or user profile in Amazon SageMaker Studio and use Amazon SageMaker Data Wrangler for data preparation, then this post is for you. In this post, we show how you can create a Data Wrangler flow and use it for data preparation in a Studio environment with a default lifecycle configuration.
Data Wrangler is a capability of Amazon SageMaker that makes it faster for data scientists and engineers to prepare data for machine learning (ML) applications via a visual interface. Data preparation is a crucial step of the ML lifecycle, and Data Wrangler provides an end-to-end solution to import, explore, transform, featurize, and process data for ML in a visual, low-code experience. It lets you easily and quickly connect to AWS components like Amazon Simple Storage Service (Amazon S3), Amazon Athena, Amazon Redshift, and AWS Lake Formation, and external sources like

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