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This guide walks you through installing, licensing, and connecting the CData Python Connector to live Vercel data. You will learn to:
Let's begin.
Python Dependencies Note: Make sure you have Python installed. The CData Python Connector supports Python versions 3.8, 3.9, 3.10, 3.11, and 3.12. If you are using a version outside this range, you may need to create a virtual environment with virtualenv.
pip install cdata_api_connector-24.0.9111-cp312-cp312-win_amd64.whl
pip install cdata_api_connector-24.0.####-python3.tar.gz
After your purchase, you should have received your license key via email from the CData Orders Team. The license key is a 25-character code that looks like this: XXXXX-XXXXX-XXXXX-XXXXX-XXXXX
.\license-installer.exe [YOUR LICENSE KEY HERE]
./install-license.sh [YOUR LICENSE KEY HERE]
Can I use my license on multiple machines?
Yes, depending on your subscription tier. Check your order confirmation email or contact your account representative for details.
If you are unsure who your account representative is, contact [email protected].
I lost my license key. How do I retrieve it?
Email [email protected] with your order number, and we will resend your license key.
Can I transfer my license to a different machine?
Yes. You will need to submit a License Transfer Request using our license transfer request page linked below:
https://www.cdata.com/lic/transfer/
After your License Transfer Request is submitted and processed, an additional activation will be added to your Product Key.
You will then be able to activate the full license on the new machine.
Once this process is complete, the license on the previous machine will become invalid.
For additional licensing questions, contact [email protected]. You can view and manage your license through our self-service portal at portal.cdata.com.
After the installation and license activation are complete, you can establish a connection using the CData Python Connector.
The CData Python Connector for Vercel is exposed as a Python module that you can import using the standard import statement and then build your application code around it.
The Connector also includes built-in metadata tools such as sys_tables and sys_tablecolumns, which allow you to perform schema discovery — including available tables, columns, and structural metadata for Vercel data.
The following example establishes a connection to Vercel using your authentication properties and retrieves column names from a specific table.
Replace or modify the connection string values below with your actual credentials, and update your table name in '[TABLE NAME]' as needed.
If your Vercel instance uses MFA or additional security requirements, you may need to include properties such as Passcode or SecurityToken in your connection string. Refer to the Connection String Options section in the Connector Help documentation (also available inside the help directory of the Connector) for a complete list of supported properties.
import cdata.api as mod
# Establish the connection using your configured properties
conn = mod.connect(
"Profile=C:\profiles\Vercel.apip;"
"AuthScheme=APIKey;"
"APIKey=your_access_token;"
)
# Query column names for the specified table
cur = conn.cursor()
cur.execute("SELECT ColumnName FROM sys_tablecolumns WHERE TableName = '[TABLE NAME]'")
print("Columns in your table:")
for row in cur.fetchall():
print(row[0])
cur.close()
conn.close()
This code connects to Vercel, queries the metadata catalog, and prints all column names for the table you specify. Check out the complete Connector documentation to learn how to modify the SQL query to explore additional schemas, tables, or other supported metadata views.
Vercel uses Bearer token authentication. You can use either a personal access token or an OAuth access token as the API key.
To obtain a personal access token:
After obtaining your token, set the following connection properties:
Profile=C:\profiles\Vercel.apip;AuthScheme=APIKey;APIKey=your_access_token;
Many Vercel resources are scoped to a team. To scope all requests to a specific team, set the TeamId connection property to your team's ID. You can find your team ID by querying the Teams table or from the Vercel dashboard. Alternatively, you can specify TeamId in your SQL queries using the WHERE clause where supported.
Once the authentication is configured, you can connect to Vercel and query data from any of the available tables such as Projects, Deployments, Teams, and Domains.
Solution: Verify that your User, Password, and any additional authentication properties required by Vercel are correct. If your data source enforces MFA, SSO, or passcodes, ensure the correct properties are included in the connection string. Refer to the complete Connector documentation for the full list of supported authentication properties, or contact [email protected] for assistance validating authentication settings.
Solution: Confirm that the endpoint URL in your connection string is correct and that outbound HTTPS traffic is allowed from your environment. If you are behind a firewall or proxy, ensure that Python is permitted to reach the service URL. For network configuration details or port requirements, contact [email protected].
Solution: Verify the Database, Schema, and table name in your SQL query. Use metadata views such as sys_tables and sys_tablecolumns to confirm the exact table and column names exposed by Vercel data. If the table name is case-sensitive, ensure you are using the correct casing in your query.
Solution: Ensure the Python Connector is installed in the correct environment. Run pip list to verify that the connector (cdata-api-connector) is present. If you are using virtual environments, activate the correct environment before executing your script.
Solution: Incorrect property formatting or missing semicolons can prevent the connector from parsing your connection settings. Review your connection string to ensure each property follows the correct Key=Value; format. Refer to the Python Connector documentation for property names supported by Vercel.
For additional connection troubleshooting, contact [email protected] with your full error message (masking sensitive credentials before sending).
With the connector installed and your connection configured, you can now begin working with live Vercel data in Python. Explore the resources below to extend your integration and build complex workflows.
| Python Client | Article Title |
|---|---|
| Python MCP Server | Connect Vercel to AI Assistants With the CData Python MCP Server |
| pandas | Use pandas to Visualize Vercel in Python |
| Dash | Use Dash & Python to Build Web Apps on Vercel |
| SQLAlchemy | Use SQLAlchemy ORMs to Access Vercel in Python |
| petl | Extract, Transform, and Load Vercel in Python |
If you need assistance at any point:
SELECT * FROM sys_tables;
If your question is not covered in this FAQ, contact [email protected].
Connect to live data from Vercel with the API Driver
Connect to Vercel