Getting Started with the CData Connect AI Python SDK for Bullhorn CRM

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 Bullhorn CRM data with standard Python database code. Because Connect AI provides the connectivity: you install one package, authenticate with a Personal Access Token, and query Bullhorn CRM (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 Bullhorn CRM in Connect AI, generating a Personal Access Token, installing the SDK, and reading (and, where supported, writing) live Bullhorn CRM data.

Prerequisites

• An account in CData Connect AI
• Python 3.8 or higher
• An active Bullhorn CRM account with valid credentials

Connect to Bullhorn CRM 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 "Bullhorn CRM" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Bullhorn CRM.

   Begin by providing your Bullhorn CRM account credentials in the following:

   • DataCenterCode: Set this to the data center code which responds to your data center. Refer to the list of data-center-specific Bullhorn API URLs: https://bullhorn.github.io/Data-Center-URLs/

   If you are uncertain about your data center code, codes like CLS2, CLS21, etc. are cluster IDs that are contained in a user's browser URL (address bar) once they are logged in.

   Example: https://cls21.bullhornstaffing.com/BullhornSTAFFING/MainFrame.jsp?#no-ba... indicates that the logged in user is on CLS21.

   Authenticating with OAuth

   Bullhorn CRM uses the OAuth 2.0 authentication standard. To authenticate using OAuth, create and configure a custom OAuth app. See the Help documentation for more information.

   
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, BullhornCRM1).

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 Id, CandidateName "
    "FROM [BullhornCRM1].[BullhornCRM].[Candidate] "
    "LIMIT 10"
)

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

Write Back to Bullhorn CRM

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 [BullhornCRM1].[BullhornCRM].[Candidate] (Id) "
    "VALUES (%(newvalue)s)",
    {"newvalue": "Example value"},
)
print(f"Rows inserted: {cur.rowcount}")

# Update existing records
cur.execute(
    "UPDATE [BullhornCRM1].[BullhornCRM].[Candidate] "
    "SET CandidateName = %(newvalue)s "
    "WHERE CandidateName = 'Jane Doe'",
    {"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 Bullhorn CRM 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 Bullhorn CRM data from Python through the CData Connect AI Python SDK. For more information on connecting to Bullhorn CRM (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live Bullhorn CRM data in Python.