Preparing and importing bulk data using Amazon SageMaker Data Wrangler
Important
As you use Data Wrangler, you incur SageMaker costs. For a complete list of charges and prices, see the Data Wrangler
tab of Amazon SageMaker pricing
After you create a dataset group, you can use Amazon SageMaker Data Wrangler (Data Wrangler) to import data from 40+ sources into an Amazon Personalize dataset. Data Wrangler is a feature of Amazon SageMaker Studio Classic that provides an end-to-end solution to import, prepare, transform, and analyze data. You can't use Data Wrangler to prepare and import data into an Actions dataset or Action interactions dataset.
When you use Data Wrangler to prepare and import data, you use a data flow. A data flow defines a series of machine learning data prep steps, starting with importing data. Each time you add a step to your flow, Data Wrangler takes an action on your data, such as transforming it or generating a visualization.
The following are some of the steps that you can add to your flow to prepare data for Amazon Personalize:
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Insights: You can add Amazon Personalize specific insight steps to your flow. These insights can help you learn about your data and what actions you can take to improve it.
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Visualizations: You can add visualization steps to generate graphs such as histograms and scatter plots. Graphs can help you discover issues in your data, such as outliers or missing values.
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Transformations: You can use Amazon Personalize specific and general transformation steps to make sure your data meets Amazon Personalize requirements. The Amazon Personalize transformation helps you map your data columns to required columns depending on the Amazon Personalize dataset type.
If you need to leave Data Wrangler before importing data into Amazon Personalize, you can return to where you left off by choosing the same dataset type when you launch Data Wrangler from the Amazon Personalize console. Or you can access Data Wrangler directly through SageMaker Studio Classic.
We recommend you import data from Data Wrangler into Amazon Personalize as follows. The transformation, visualization and analysis steps are optional, repeatable, and can be completed in any order.
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Set up permissions - Set up permissions for Amazon Personalize and SageMaker service roles. And set up permissions for your users.
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Launch Data Wrangler in SageMaker Studio Classic from the Amazon Personalize console - Use the Amazon Personalize console to configure a SageMaker domain and launch Data Wrangler in SageMaker Studio Classic.
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Import your data into Data Wrangler - Import data from 40+ sources into Data Wrangler. Sources include AWS services, such as Amazon Redshift, Amazon EMR, or Amazon Athena, and 3rd parties such as Snowflake or DataBricks.
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Transform your data - Use Data Wrangler to transform your data to meet Amazon Personalize requirements.
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Visualize and analyze your data - Use Data Wrangler to visualize your data and analyze it through Amazon Personalize specific insights.
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Process and import data into Amazon Personalize - Use a SageMaker Studio Classic Jupyter notebook to import your processed data into Amazon Personalize.
Additional information
The following resources provide additional information about using Amazon SageMaker Data Wrangler and Amazon Personalize.
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For a tutorial that walks you through processing and transforming a sample dataset, see Demo: Data Wrangler Titanic Dataset Walkthrough in the Amazon SageMaker Developer Guide. This tutorial introduces the fields and functions of Data Wrangler.
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For information on onboarding to Amazon SageMaker domains, see Quick onboard to Amazon SageMaker Domain in the Amazon SageMaker Developer Guide.
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For information on Amazon Personalize data requirements, see Preparing training data for Amazon Personalize.
