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We’re excited to announce Amazon SageMaker Data Wrangler support for Amazon S3 Access Points. With its visual point and click interface, SageMaker Data Wrangler simplifies the process of data preparation and feature engineering including data selection, cleansing, exploration, and visualization, while S3 Access Points simplifies data access by providing unique hostnames with specific access policies.
Starting today, SageMaker Data Wrangler is making it easier for users to prepare data from shared datasets stored in Amazon Simple Storage Service (Amazon S3) while enabling organizations to securely control data access in their organization. With S3 Access Points, data administrators can now create application- and team-specific access points to facilitate data sharing, rather than managing complex bucket policies with many different permission rules.
In this post, we walk you through importing data from, and exporting data to, an S3 access point in SageMaker Data Wrangler.
Imagine you, as an administrator, have to manage data for multiple data science teams running their own data preparation workflows in SageMaker Data Wrangler. Administrators often face three challenges:
With traditional bucket policies, you would struggle setting up granular access because bucket policies apply the same permissions to all objects within the bucket. Traditional bucket policies also can’t support securing access at the endpoint level.
S3 Access Points solves these problems by granting fine-grained access control at a granular level, making it easier to manage permissions for different teams without impacting other parts of the bucket. Instead of modifying a single bucket policy, you can create multiple access points with individual policies tailored to specific use cases, reducing the risk of misconfiguration or unintended access to sensitive data. Lastly, you can enforce endpoint policies on access points to define rules that control which VPCs or IP addresses can access the data through a specific access point.
We demonstrate how to use S3 Access Points with SageMaker Data Wrangler with the following steps:
For this post, we use the Bank Marketing dataset for our sample data. However, you can use any other dataset you prefer.
For this walkthrough, you should have the following prerequisites:
Upload your data to an S3 bucket. For instructions, refer to Uploading objects. For this post, we use the Bank Marketing dataset.
To create an S3 access point, complete the following steps. For more information, refer to Creating access points.
On the access point details page, note the Amazon Resource Name (ARN) and access point alias. You use these later when you interact with the access point in SageMaker Data Wrangler.
If you have a SageMaker Studio domain up and ready, complete the following steps to edit the execution role:
By default, the IAM role that you use to access Data Wrangler is SageMakerExecutionRole
. We need to add the following two policies to use S3 access points:
PutObject
, GetObject
, and DeleteObject
: To create a new SageMaker Data Wrangler flow, complete the following steps:
For this post, we use the ARN to import data using the S3 access point. However, the ARN only works for S3 access points and SageMaker Studio domains within the same Region.
Alternatively, you can use the alias, as shown in the following screenshot. Unlike ARNs, aliases can be referenced across Regions.
After we complete the necessary transformations, we can export the results to the S3 access point. In our case, we simply dropped a column. When you complete whatever transformations you need for your use case, complete the following steps:
Now you have used S3 access points to import and export data securely and efficiently without having to manage complex bucket policies and navigate multiple folder structures.
If you created a new SageMaker domain to follow along, be sure to stop any running apps and delete your domain to stop incurring charges. Also, delete any S3 access points and delete any S3 buckets.
In this post, we introduced the availability of S3 Access Points for SageMaker Data Wrangler and showed you how you can use this feature to simplify data control within SageMaker Studio. We accessed the dataset from, and saved the resulting transformations to, an S3 access point alias across AWS accounts. We hope that you take advantage of this feature to remove any bottlenecks with data access for your SageMaker Studio users, and encourage you to give it a try!
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