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How to use SQLAlchemy ORM to access Dynamics 365 Data in Python
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).
About Dynamics 365 Data Integration
CData simplifies access and integration of live Microsoft Dynamics 365 data. Our customers leverage CData connectivity to:
- Read and write data in the full Dynamics 365 ecosystem: Sales, Customer Service, Finance & Operations, Marketing, and more.
- Extend the native features of Dynamics CRM with customizable caching and intelligent query aggregation and separation.
- Authenticate securely with Dynamics 365 in a variety of ways, including Azure Active Directory, Azure Managed Service Identity credentials, and Azure Service Principal using either a client secret or a certificate.
- Use SQL stored procedures to manage their Dynamics 365 entities - listing, creating, and removing associations between entities.
CData customers use our Dynamics 365 connectivity solutions for a variety of reasons, whether they're looking to replicate their data into a data warehouse (alongside other data sources)or analyze live Dynamics 365 data from their preferred data tools inside the Microsoft ecosystem (Power BI, Excel, etc.) or with external tools (Tableau, Looker, etc.).
Getting Started
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, https://orgcb42e1d0.crm.dynamics.com
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 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 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=https://myaccount.operations.dynamics.com/&Edition=Sales&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
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=https://myaccount.operations.dynamics.com/&Edition=Sales&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
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(GoalHeadings_table.select().where(GoalHeadings_table.c.Name == "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 CData Python Connector for Dynamics 365 to start building Python apps and scripts with connectivity to Dynamics 365 data. Reach out to our Support Team if you have any questions.
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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.
