Getting Started with the CData Connect AI Python SDK for Dynamics 365 Business Central

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 Business Central 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 Business Central (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 Business Central in Connect AI, generating a Personal Access Token, installing the SDK, and reading (and, where supported, writing) live Dynamics 365 Business Central data.

Prerequisites

• An account in CData Connect AI
• Python 3.8 or higher
• An active Dynamics 365 Business Central account with valid credentials

Connect to Dynamics 365 Business Central in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Dynamics 365 Business Central" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Dynamics 365 Business Central.

   To authenticate to Dynamics 365 Business Central, you must select an AuthScheme and provide the required properties (OAuth by default).

   All Methods

   Specify the OrganizationUrl. If you have multiple companies in your organization, you must also specify the Company to indicate which company you would like to connect to. Company does not need to be specified if you have only one company.

   Access Key

   To authenticate with an Access Key, set AuthScheme to "AccessKey" and provide the User and AccessKey properties.

   To obtain the User and AccessKey values, navigate to the Users page in Dynamics 365 Business Central and then click on Edit. The User Name and Web Service Access Key values are what you will enter as the User and AccessKey connection string properties. Note that the User Name is not your email address. It is a shortened user name.

   If you wish to authenticate through other methods, refer to the Help documentation.

   
4. Click Save & Test
5. 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.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. 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, D365BusinessCentral1).

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 accountid, Name "
    "FROM [D365BusinessCentral1].[D365BusinessCentral].[Accounts] "
    "LIMIT 10"
)

for row in cur.fetchall():
    print(row)

Write Back to Dynamics 365 Business Central

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 [D365BusinessCentral1].[D365BusinessCentral].[Accounts] (accountid) "
    "VALUES (%(newvalue)s)",
    {"newvalue": "Example value"},
)
print(f"Rows inserted: {cur.rowcount}")

# Update existing records
cur.execute(
    "UPDATE [D365BusinessCentral1].[D365BusinessCentral].[Accounts] "
    "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 Business Central 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 Business Central data from Python through the CData Connect AI Python SDK. For more information on connecting to Dynamics 365 Business Central (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live Dynamics 365 Business Central data in Python.