Data Cloud Python Connector

Use the Data Cloud Python Connector to extract and analyze your Data Cloud data in Python. The connector enables you to:

  • Query Data Cloud data using SQL
  • Work with data in Pandas DataFrames
  • Create visual data models
  • Perform analytical operations
  • Build machine learning and AI models

Install the connector from PyPI:

After successful installation, you'll see: Successfully Installed salesforce-cdp-connector-<version>

Choose one of two authentication methods:

  1. Create a connected app:

    • Go to Set up > App Manager > New Connected App
    • Complete the basic information
    • Enable OAuth settings
    • Enter your callback URL
    • Select required OAuth scopes
    • Save and continue

  2. Get your credentials:

    • Copy the consumer key (client ID)
    • Copy the consumer secret

  1. Create a connected app (same steps as Method 1)

  2. Select these OAuth scopes:

    • refresh_token
    • api
    • cdp_query_api
    • cdp_profile_api

  3. Get your OAuth tokens:

    • Construct the authorization URL:
    • Get the login URL from Set up > My Domain
    • Get the callback URL from Set up > App Manager > View Connected App > Call Back URL
    • Open the URL in your browser
    • Extract the authorization code from the redirect URL
    • Make a POST request to get tokens:
    • Save the access_token and refresh_token from the response

Create a cursor and execute your SQL query:

Choose one of three methods to retrieve your data:

After setting up the Python connector, here are some recommended next steps:

  • Use the connector to query your Data Cloud tables
  • Examine the schema of your data model objects
  • Try different SQL queries to understand your data structure
  • Create Pandas DataFrames for data analysis
  • Use Python libraries like matplotlib or seaborn for visualization
  • Perform statistical analysis on your data
  • Connect the connector to your existing Python applications
  • Set up automated data extraction workflows
  • Integrate with your data pipeline tools
  • Use connection pooling for better performance
  • Implement proper error handling
  • Follow security best practices for credential management
  • Monitor your API usage and stay within limits