Amazon SageMaker Data Wrangler makes it faster for data scientists and engineers to prepare data for machine learning (ML) applications by using a visual interface. Previously, when you created a Data Wrangler data flow, you could choose different export options to easily integrate that data flow into your data processing pipeline. Data Wrangler offers export options to Amazon Simple Storage Service (Amazon S3), SageMaker Pipelines, and SageMaker Feature Store, or as Python code. The export options create a Jupyter notebook and require you to run the code to start a processing job facilitated by SageMaker Processing.
We’re excited to announce the general release of destination nodes and the Create Job feature in Data Wrangler. This feature gives you the ability to export all the transformations that you made to a dataset to a destination node with just a few clicks. This allows you to create data processing jobs and export

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