Data 360 Python Connector

Use the Data 360 Python Connector to extract and analyze your Data 360 data in Python. The connector enables you to:

• Query Data 360 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:

1pip install salesforce-cdp-connector

Choose one of two authentication methods:

1. Create an external client app:

   • Go to Set up > App Manager > New External Client 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 an external client 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:
     1<LOGIN_URL>/services/oauth2/authorize?response_type=code&client_id=<client_id>&redirect_uri=<callback_url>

   • Get the login URL from Set up > My Domain
   • Get the callback URL from Set up > App Manager > View External Client 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:
     1<YOUR_ORG_URL>/services/oauth2/token?code=<CODE>&grant_type=authorization_code&client_id=<clientId>&client_secret=<clientSecret>&redirect_uri=<callback_uri>

   • Save the access_token and refresh_token from the response

1from salesforcecdpconnector.connection import SalesforceCDPConnection
2
3conn = SalesforceCDPConnection(
4    login_url='your_org_url',
5    user_name='your_username',
6    password='your_password',
7    client_id='your_consumer_key',
8    client_secret='your_consumer_secret'
9)

1from salesforcecdpconnector.connection import SalesforceCDPConnection
2
3conn = SalesforceCDPConnection(
4    login_url='your_org_url',
5    client_id='your_consumer_key',
6    client_secret='your_consumer_secret',
7    core_token='your_access_token',
8    refresh_token='your_refresh_token'
9)

Create a cursor and execute your SQL query:

1cur = conn.cursor()
2cur.execute('SELECT * FROM your_table')

Choose one of three methods to retrieve your data:

1result = cur.fetchone()

1results = cur.fetchall()

1df = conn.get_pandas_dataframe('SELECT * FROM your_table')

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

• Use the connector to query your Data 360 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

• Learn about Data 360 Query API
• Explore Data 360 data model objects
• Understand Data 360 limits and guidelines

• 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

• Join the Salesforce Developer Community
• Check out Trailhead modules on Data 360
• Explore GitHub examples for Data 360 integration

j

Query Data in Data 360

Get Started With Data 360 SQL

Get Started with Data 360 JDBC

Get Started with the Power BI Connector

Get Started with the Python Connector