How to use SQLAlchemy ORM to access Salesforce CRM Analytics 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 CRM Analytics and the SQLAlchemy toolkit, you can build Salesforce CRM Analytics-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Salesforce CRM Analytics data to query, update, delete, and insert Salesforce CRM Analytics data.

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

Connecting to Salesforce CRM Analytics Data

Connecting to Salesforce CRM Analytics 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 CRM Analytics uses the OAuth 2 authentication standard. Obtain the OAuthClientId and OAuthClientSecret by registering an app with Salesforce CRM Analytics.

See the Getting Started section of the Help documentation for an authentication guide.

Multi-Factor Authentication (MFA)

If the connected Salesforce org has MFA enforcement enabled, set MFACode to the time-based one-time passcode (TOTP) generated by your authenticator app (such as Salesforce Authenticator or Google Authenticator). MFACode applies alongside the standard OAuth flow.

Follow the procedure below to install SQLAlchemy and start accessing Salesforce CRM Analytics 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 CRM Analytics Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Salesforce CRM Analytics 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("salesforcecrmanalytics:///?OAuthClientId=MyConsumerKey&OAuthClientSecret=MyConsumerSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&MFACode=YourMFACode")

Declare a Mapping Class for Salesforce CRM Analytics 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 Dataset_Opportunity 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 Dataset_Opportunity(base):
	__tablename__ = "Dataset_Opportunity"
	Name = Column(String,primary_key=True)
	CloseDate = Column(String)
	...

Query Salesforce CRM Analytics 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("salesforcecrmanalytics:///?OAuthClientId=MyConsumerKey&OAuthClientSecret=MyConsumerSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&MFACode=YourMFACode")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Dataset_Opportunity).filter_by(StageName="Closed Won"):
	print("Name: ", instance.Name)
	print("CloseDate: ", instance.CloseDate)
	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

Dataset_Opportunity_table = Dataset_Opportunity.metadata.tables["Dataset_Opportunity"]
for instance in session.execute(Dataset_Opportunity_table.select().where(Dataset_Opportunity_table.c.StageName == "Closed Won")):
	print("Name: ", instance.Name)
	print("CloseDate: ", instance.CloseDate)
	print("---------")

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

Insert Salesforce CRM Analytics Data

To insert Salesforce CRM Analytics data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Salesforce CRM Analytics.

new_rec = Dataset_Opportunity(Name="placeholder", StageName="Closed Won")
session.add(new_rec)
session.commit()

Update Salesforce CRM Analytics Data

To update Salesforce CRM Analytics data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Salesforce CRM Analytics.

updated_rec = session.query(Dataset_Opportunity).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.StageName = "Closed Won"
session.commit()

Delete Salesforce CRM Analytics Data

To delete Salesforce CRM Analytics data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Dataset_Opportunity).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

Free Trial & More Information

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