Access Salesforce data in your Databricks notebook using a Python connector. The
user-friendly interface lets you quickly execute queries against data in Data 360 so that you can extract the data that you need into Python.
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 Databricks notebook, install the CDP Python Connector to access Data 360 objects and ingest Salesforce data into Databricks.
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As part of the Python connector configuration, create an external client app or
connected app.
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Restrict access to IP addresses to manage access to your data.
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Use a key-based authentication to securely access your data.
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In Data 360, connect to your data source to query the dataset in
Databricks. For example, to get email engagement data, you need access to the Email
Engagement object in Marketing Cloud Engagement.
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To consume the inferences in Data 360, upload the Python file to your
notebook.
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To get your model endpoint, go to the Databricks dashboard in the Registered Models -
Serving section. 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.