Getting Started with the CData Connect AI Python SDK for Dynamics 365
The CData Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client that lets you fetch and act on live Dynamics 365 data with standard Python database code. Because Connect AI provides the connectivity: you install one package, authenticate with a Personal Access Token, and query Dynamics 365 (and every other source connected in Connect AI) using the same familiar connect() / cursor() / fetchall() pattern you already know from libraries like sqlite3 and psycopg2.
This guide walks through connecting Dynamics 365 in Connect AI, generating a Personal Access Token, installing the SDK, and reading (and, where supported, writing) live Dynamics 365 data.
Prerequisites
- An account in CData Connect AI
- Python 3.8 or higher
- An active Dynamics 365 account with valid credentials
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 Microsoft Entra ID, 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
Connect to Dynamics 365 in Connect AI
CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.
- Log into Connect AI, click Sources, and then click Add Connection
- Select "Dynamics 365" from the Add Connection panel
-
Enter the necessary authentication properties to connect to Dynamics 365.
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
- Click Save & Test
- Navigate to the Permissions tab and update the user-based permissions.
Generate a Personal Access Token (PAT)
The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.
- Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
- On the Settings page, go to the Access Tokens section and click Create PAT.
- Give the PAT a name and click Create.
- The PAT is only visible at creation, so copy it and store it securely.
Install the SDK
Install the SDK from PyPI with pip:
pip install cdata-connect-ai
Connect and Run Your First Query
Connect with your account email and PAT, then query sys_tables to discover every table available across your connected sources. Identifiers in Connect AI are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, Dynamics3651).
import cdata_connect_ai
conn = cdata_connect_ai.connect(
username="[email protected]",
password="<your_pat>",
)
cur = conn.cursor()
# Discover what's available across your connected sources
cur.execute("SELECT CatalogName, SchemaName, TableName FROM sys_tables LIMIT 25")
for row in cur.fetchall():
print(row)
Pick any table from the results and query it directly:
cur.execute(
"SELECT GoalHeadingId, Name "
"FROM [Dynamics3651].[Dynamics365].[GoalHeadings] "
"LIMIT 10"
)
for row in cur.fetchall():
print(row)
Write Back to Dynamics 365
When the data source and your connection permissions allow it, the same cursor runs INSERT, UPDATE, and DELETE statements. Bind values with pyformat (%(name)s) parameters, exactly as you would for a filtered read, and check cursor.rowcount for the number of affected rows.
# Insert a new record
cur.execute(
"INSERT INTO [Dynamics3651].[Dynamics365].[GoalHeadings] (GoalHeadingId) "
"VALUES (%(newvalue)s)",
{"newvalue": "Example value"},
)
print(f"Rows inserted: {cur.rowcount}")
# Update existing records
cur.execute(
"UPDATE [Dynamics3651].[Dynamics365].[GoalHeadings] "
"SET Name = %(newvalue)s "
"WHERE Name = 'MyAccount'",
{"newvalue": "Updated value"},
)
print(f"Rows updated: {cur.rowcount}")
conn.close()
Note: Even for writable sources, a read-only PAT or connection permission will reject write operations. The same parameterized pattern also covers DELETE statements and stored procedures through cursor.callproc().
That is the entire workflow: one package, a PAT, and standard DB-API calls. Because the SDK returns a normal DB-API connection, it drops straight into the rest of the Python data ecosystem. From here you can load Dynamics 365 data into pandas, build ETL pipelines with petl, or power a Dash web app, all using this same connection.
More Information and Free Trial
Now you can query live Dynamics 365 data from Python through the CData Connect AI Python SDK. For more information on connecting to Dynamics 365 (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live Dynamics 365 data in Python.