Use SQLAlchemy ORMs to Access Dynamics 365 Data in Python

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Dynamics 365 Python Connector

Python Connector Libraries for Dynamics 365 Data Connectivity. Integrate Dynamics 365 with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Dynamics 365 enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Dynamics 365 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 365 and the SQLAlchemy toolkit, you can build Dynamics 365-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Dynamics 365 data to query, update, delete, and insert Dynamics 365 data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Dynamics 365 data in Python. When you issue complex SQL queries from Dynamics 365, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Dynamics 365 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Dynamics 365 Data

Connecting to Dynamics 365 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.

Edition and OrganizationUrl are required connection properties. The Dynamics 365 connector supports connecting to the following editions: CustomerService, FieldService, FinOpsOnline, FinOpsOnPremise, HumanResources, Marketing, ProjectOperations and Sales.

For Dynamics 365 Business Central, use the separate Dynamics 365 Business Central driver.

OrganizationUrl is the URL to your Dynamics 365 organization. For instance,

Follow the procedure below to install SQLAlchemy and start accessing Dynamics 365 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 Dynamics 365 Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Dynamics 365 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("dynamics365:///?OrganizationUrl=")

Declare a Mapping Class for Dynamics 365 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 GoalHeadings 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 GoalHeadings(base): __tablename__ = "GoalHeadings" GoalHeadingId = Column(String,primary_key=True) Name = Column(String) ...

Query Dynamics 365 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("dynamics365:///?OrganizationUrl=") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(GoalHeadings).filter_by(Name="MyAccount"): print("GoalHeadingId: ", instance.GoalHeadingId) 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

GoalHeadings_table = GoalHeadings.metadata.tables["GoalHeadings"] for instance in session.execute( == "MyAccount")): print("GoalHeadingId: ", instance.GoalHeadingId) 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 Dynamics 365 Data

To insert Dynamics 365 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 365.

new_rec = GoalHeadings(GoalHeadingId="placeholder", Name="MyAccount") session.add(new_rec) session.commit()

Update Dynamics 365 Data

To update Dynamics 365 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 365.

updated_rec = session.query(GoalHeadings).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Name = "MyAccount" session.commit()

Delete Dynamics 365 Data

To delete Dynamics 365 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(GoalHeadings).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 Dynamics 365 Python Connector to start building Python apps and scripts with connectivity to Dynamics 365 data. Reach out to our Support Team if you have any questions.