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

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

Connecting to Salesloft Data

Connecting to Salesloft 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.

SalesLoft authenticates using the OAuth authentication standard or an API Key. OAuth requires the authenticating user to interact with SalesLoft using the browser.

Using OAuth

For OAuth authentication, create an OAuth app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the OAuth section in the Help documentation for an authentication guide.

Using APIKey

Alternatively, you can authenticate with an APIKey. Provision an API key from the SalesLoft user interface: https://accounts.salesloft.com/oauth/applications/. You will receive a Key which will be used when issuing requests.

Follow the procedure below to install SQLAlchemy and start accessing Salesloft 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 Salesloft Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Salesloft 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("salesloft:///?AuthScheme=OAuth&OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackUrl=http://localhost:33333&InitiateOAuth=GETANDREFRESH")

Declare a Mapping Class for Salesloft 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 Accounts 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 Accounts(base):
	__tablename__ = "Accounts"
	Id = Column(String,primary_key=True)
	Name = Column(String)
	...

Query Salesloft 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("salesloft:///?AuthScheme=OAuth&OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackUrl=http://localhost:33333&InitiateOAuth=GETANDREFRESH")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Accounts).filter_by(Country="Canada"):
	print("Id: ", instance.Id)
	print("Name: ", instance.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

Accounts_table = Accounts.metadata.tables["Accounts"]
for instance in session.execute(Accounts_table.select().where(Accounts_table.c.Country == "Canada")):
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	print("---------")

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

Insert Salesloft Data

To insert Salesloft 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 Salesloft.

new_rec = Accounts(Id="placeholder", Country="Canada")
session.add(new_rec)
session.commit()

Update Salesloft Data

To update Salesloft 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 Salesloft.

updated_rec = session.query(Accounts).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Country = "Canada"
session.commit()

Delete Salesloft Data

To delete Salesloft 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(Accounts).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 Salesloft to start building Python apps and scripts with connectivity to Salesloft data. Reach out to our Support Team if you have any questions.