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

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How to use SQLAlchemy ORM to access Dynamics CRM Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Dynamics CRM data.

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

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

Connecting to Dynamics CRM Data

Connecting to Dynamics CRM 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.

The connection string options meet the authentication and connection requirements of different Dynamics CRM instances. To connect to your instance, set the User and Password properties, under the Authentication section, to valid Dynamics CRM user credentials and set the Url to a valid Dynamics CRM server organization root. Additionally, set the CRMVersion property to 'CRM2011+' or 'CRMOnline'. IFD configurations are supported as well; set InternetFacingDeployment to true.

Additionally, you can provide the security token service (STS) or AD FS endpoint in the STSURL property. This value can be retrieved with the GetSTSUrl stored procedure. Office 365 users can connect to the default STS URL by simply setting CRMVersion.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Dynamics CRM 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("dynamicscrm:///?User=myuseraccount&Password=mypassword&URL=https://myOrg.crm.dynamics.com/&CRM Version=CRM Online")

Declare a Mapping Class for Dynamics CRM 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" FirstName = Column(String,primary_key=True) NumberOfEmployees = Column(String) ...

Query Dynamics CRM 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("dynamicscrm:///?User=myuseraccount&Password=mypassword&URL=https://myOrg.crm.dynamics.com/&CRM Version=CRM Online") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Account).filter_by(FirstName="Bob"): print("FirstName: ", instance.FirstName) print("NumberOfEmployees: ", instance.NumberOfEmployees) 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.FirstName == "Bob")): print("FirstName: ", instance.FirstName) print("NumberOfEmployees: ", instance.NumberOfEmployees) print("---------")

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

Insert Dynamics CRM Data

To insert Dynamics CRM 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 Dynamics CRM.

new_rec = Account(FirstName="placeholder", FirstName="Bob") session.add(new_rec) session.commit()

Update Dynamics CRM Data

To update Dynamics CRM 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 Dynamics CRM.

updated_rec = session.query(Account).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.FirstName = "Bob" session.commit()

Delete Dynamics CRM Data

To delete Dynamics CRM 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 Dynamics CRM to start building Python apps and scripts with connectivity to Dynamics CRM data. Reach out to our Support Team if you have any questions.