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Get the Report →How to use SQLAlchemy ORM to access Microsoft Dataverse Data in Python
Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Microsoft Dataverse data.
The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Microsoft Dataverse and the SQLAlchemy toolkit, you can build Microsoft Dataverse-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Microsoft Dataverse data to query, update, delete, and insert Microsoft Dataverse data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Microsoft Dataverse data in Python. When you issue complex SQL queries from Microsoft Dataverse, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Microsoft Dataverse and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Microsoft Dataverse Data
Connecting to Microsoft Dataverse 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.
You can connect without setting any connection properties for your user credentials. Below are the minimum connection properties required to connect.
- InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
- OrganizationUrl: Set this to the organization URL you are connecting to, such as https://myorganization.crm.dynamics.com.
- Tenant (optional): Set this if you wish to authenticate to a different tenant than your default. This is required to work with an organization not on your default Tenant.
When you connect the Common Data Service OAuth endpoint opens in your default browser. Log in and grant permissions. The OAuth process completes automatically.
Follow the procedure below to install SQLAlchemy and start accessing Microsoft Dataverse 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 Microsoft Dataverse Data in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Microsoft Dataverse 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("cds:///?OrganizationUrl=https://myaccount.crm.dynamics.com/InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
Declare a Mapping Class for Microsoft Dataverse 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"
AccountId = Column(String,primary_key=True)
Name = Column(String)
...
Query Microsoft Dataverse 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("cds:///?OrganizationUrl=https://myaccount.crm.dynamics.com/InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Accounts).filter_by(Name="MyAccount"):
print("AccountId: ", instance.AccountId)
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.Name == "MyAccount")):
print("AccountId: ", instance.AccountId)
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 Microsoft Dataverse Data
To insert Microsoft Dataverse 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 Microsoft Dataverse.
new_rec = Accounts(AccountId="placeholder", Name="MyAccount")
session.add(new_rec)
session.commit()
Update Microsoft Dataverse Data
To update Microsoft Dataverse 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 Microsoft Dataverse.
updated_rec = session.query(Accounts).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Name = "MyAccount"
session.commit()
Delete Microsoft Dataverse Data
To delete Microsoft Dataverse 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 Microsoft Dataverse to start building Python apps and scripts with connectivity to Microsoft Dataverse data. Reach out to our Support Team if you have any questions.