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Get the Report →How to use SQLAlchemy ORM to access Salesforce Data in Python
Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Salesforce 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 and the SQLAlchemy toolkit, you can build Salesforce-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Salesforce data to query, update, delete, and insert Salesforce data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Salesforce data in Python. When you issue complex SQL queries from Salesforce, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Salesforce and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
About Salesforce Data Integration
Accessing and integrating live data from Salesforce has never been easier with CData. Customers rely on CData connectivity to:
- Access to custom entities and fields means Salesforce users get access to all of Salesforce.
- Create atomic and batch update operations.
- Read, write, update, and delete their Salesforce data.
- Leverage the latest Salesforce features and functionalities with support for SOAP API versions 30.0.
- See improved performance based on SOQL support to push complex queries down to Salesforce servers.
- Use SQL stored procedures to perform actions like creating, retrieving, aborting, and deleting jobs, uploading and downloading attachments and documents, and more.
Users frequently integrate Salesforce data with:
- other ERPs, marketing automation, HCMs, and more.
- preferred data tools like Power BI, Tableau, Looker, and more.
- databases and data warehouses.
For more information on how CData solutions work with Salesforce, check out our Salesforce integration page.
Getting Started
Connecting to Salesforce Data
Connecting to Salesforce 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.
There are several authentication methods available for connecting to Salesforce: Login, OAuth, and SSO. The Login method requires you to have the username, password, and security token of the user.
If you do not have access to the username and password or do not wish to require them, you can use OAuth authentication.
SSO (single sign-on) can be used by setting the SSOProperties, SSOLoginUrl, and TokenUrl connection properties, which allow you to authenticate to an identity provider. See the "Getting Started" chapter in the help documentation for more information.
Follow the procedure below to install SQLAlchemy and start accessing Salesforce 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 in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Salesforce 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("salesforce:///?User=username&Password=password&SecurityToken=Your_Security_Token")
Declare a Mapping Class for Salesforce 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"
Industry = Column(String,primary_key=True)
AnnualRevenue = Column(String)
...
Query Salesforce 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("salesforce:///?User=username&Password=password&SecurityToken=Your_Security_Token")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Account).filter_by(Name="GenePoint"):
print("Industry: ", instance.Industry)
print("AnnualRevenue: ", instance.AnnualRevenue)
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.Name == "GenePoint")):
print("Industry: ", instance.Industry)
print("AnnualRevenue: ", instance.AnnualRevenue)
print("---------")
For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.
Insert Salesforce Data
To insert Salesforce 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.
new_rec = Account(Industry="placeholder", Name="GenePoint")
session.add(new_rec)
session.commit()
Update Salesforce Data
To update Salesforce 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.
updated_rec = session.query(Account).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Name = "GenePoint"
session.commit()
Delete Salesforce Data
To delete Salesforce 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(Account).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 to start building Python apps and scripts with connectivity to Salesforce data. Reach out to our Support Team if you have any questions.