Amazon SageMaker Data Wrangler

These example flows demonstrates how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler.

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for ML. From a single interface in SageMaker Studio, you can import data from Amazon S3, Amazon Athena, Amazon Redshift, AWS Lake Formation, and Amazon SageMaker Feature Store, and in just a few clicks SageMaker Data Wrangler will automatically load, aggregate, and display the raw data. It will then make conversion recommendations based on the source data, transform the data into new features, validate the features, and provide visualizations with recommendations on how to remove common sources of error such as incorrect labels. Once your data is prepared, you can build fully automated ML workflows with Amazon SageMaker Pipelines or import that data into Amazon SageMaker Feature Store.

The SageMaker example notebooks are Jupyter notebooks that demonstrate the usage of Amazon SageMaker.


Amazon SageMaker Data Wrangler is a feature in Amazon SageMaker Studio. Use this section to learn how to access and get started using Data Wrangler. Do the following:


Tabular Dataflow

Timeseries Dataflow

Joined Dataflow

Explore and clean tabular data in notebook