Access Salesforce data from an Amazon SageMaker notebook using the Data 360 Python connector. The intuitive interface lets you quickly execute run
queries and extract the data that you need.
Required Editions
| USER PERMISSIONS
NEEDED |
| Allow users to manage models in AI Models |
Enables you to create, update, and delete models in AI Models. |
| PERMISSION SETS |
| Data Cloud Architect |
Admin-level access to all AI Models features, including the ability to create,
update, delete, and activate models. |
| Data Cloud User |
Restricted access to use a model, including getting predictions and
improvements derived from a model. |
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In your SageMaker notebook, install the Python connector. The connector enables you to
access Data 360 objects and ingest Salesforce data into SageMaker.
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As part of the Python connector configuration, create an external client app or
connected app.
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Authenticate to securely access data using one of these methods.
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In Data 360, connect to your Salesforce data source to query the
dataset. For example, to get email engagement data, access the Email Engagement object in
Marketing Cloud Engagement.
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To use the inferences in Data 360, upload the Python file to your
notebook.
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To get the model endpoint, go to the SageMaker dashboard in the Inferences section, and
go to Endpoints. Note the URL as you need these details to connect your model with
Salesforce.
Important Creating connected apps is restricted as of Spring ‘26. You can continue to use existing connected apps during and after Spring ‘26. However, we recommend using external client apps instead. If you must continue creating connected apps, contact Salesforce Support.