Use SQLAlchemy ORMs to Access Salesforce Einstein Data in Python

Ready to get started?

Download for a free trial:

Download Now

Learn more:

Einstein Python Connector

Python Connector Libraries for Salesforce Einstein Data Connectivity. Integrate Salesforce Einstein with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Salesforce Einstein enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Salesforce Einstein data.

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

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

Connecting to Salesforce Einstein Data

Connecting to Salesforce Einstein 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 Einstein Analytics uses the OAuth 2 authentication standard. You will need to obtain the OAuthClientId and OAuthClientSecret by registering an app with Salesforce Einstein Analytics.

See the Getting Started section of the CData data provider documentation for an authentication guide.

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

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model Salesforce Einstein Data in Python

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

engine = create_engine("sfeinsteinanalytics:///?OAuthClientId=MyConsumerKey&OAuthClientSecret=MyConsumerSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Salesforce Einstein 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 Einstein 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("sfeinsteinanalytics:///?OAuthClientId=MyConsumerKey&OAuthClientSecret=MyConsumerSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
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)

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( == "Closed Won")):
	print("Name: ", instance.Name)
	print("CloseDate: ", instance.CloseDate)

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

Insert Salesforce Einstein Data

To insert Salesforce Einstein 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 Einstein.

new_rec = Dataset_Opportunity(Name="placeholder", StageName="Closed Won")

Update Salesforce Einstein Data

To update Salesforce Einstein 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 Einstein.

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

Delete Salesforce Einstein Data

To delete Salesforce Einstein 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 recoreds (rows).

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

Free Trial & More Information

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