How to use SQLAlchemy ORM to access Salesforce Data Cloud Data in Python

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Salesforce Data Cloud and the SQLAlchemy toolkit, you can build Salesforce Data Cloud-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Salesforce Data Cloud data to query Salesforce Data Cloud data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Salesforce Data Cloud data in Python. When you issue complex SQL queries from Salesforce Data Cloud, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Salesforce Data Cloud and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Salesforce Data Cloud Data

Connecting to Salesforce Data Cloud data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Salesforce Data Cloud supports authentication via the OAuth standard.

OAuth

Set to OAuth.

Desktop Applications

CData provides an embedded OAuth application that simplifies authentication at the desktop.

You can also authenticate from the desktop via a custom OAuth application, which you configure and register at the Salesforce Data Cloud console. For further information, see Creating a Custom OAuth App in the Help documentation.

Before you connect, set these properties:

• : GETANDREFRESH. You can use to avoid repeating the OAuth exchange and manually setting the .
• (custom applications only): The Client ID assigned when you registered your custom OAuth application.
• (custom applications only): The Client Secret assigned when you registered your custom OAuth application.

When you connect, the driver opens Salesforce Data Cloud's OAuth endpoint in your default browser. Log in and grant permissions to the application.

The driver then completes the OAuth process as follows:

• Extracts the access token from the callback URL.
• Obtains a new access token when the old one expires.
• Saves OAuth values in so that they persist across connections.

For other OAuth methods, including Web Applications and Headless Machines, refer to the Help documentation.

Follow the procedure below to install SQLAlchemy and start accessing Salesforce Data Cloud through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy
pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Model Salesforce Data Cloud Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Salesforce Data Cloud data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("salesforcedatacloud:///?InitiateOAuth=GETANDREFRESH")

Declare a Mapping Class for Salesforce Data Cloud Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Account table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class Account(base):
	__tablename__ = "Account"
	[Account ID] = Column(String,primary_key=True)
	[Account Name] = Column(String)
	...

Query Salesforce Data Cloud Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("salesforcedatacloud:///?InitiateOAuth=GETANDREFRESH")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Account).filter_by(EmployeeCount="250"):
	print("[Account ID]: ", instance.[Account ID])
	print("[Account Name]: ", instance.[Account Name])
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Account_table = Account.metadata.tables["Account"]
for instance in session.execute(Account_table.select().where(Account_table.c.EmployeeCount == "250")):
	print("[Account ID]: ", instance.[Account ID])
	print("[Account Name]: ", instance.[Account Name])
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Salesforce Data Cloud to start building Python apps and scripts with connectivity to Salesforce Data Cloud data. Reach out to our Support Team if you have any questions.