Getting Started with the CData Connect AI Python SDK for EnterpriseDB

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

Prerequisites

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

Connect to EnterpriseDB 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 "EnterpriseDB" from the Add Connection panel

3. Enter the necessary authentication properties to connect to EnterpriseDB.

   The following connection properties are required in order to connect to data.

   • Server: The host name or IP of the server hosting the EnterpriseDB database.
   • Port: The port of the server hosting the EnterpriseDB database.

   You can also optionally set the following:

   • Database: The default database to connect to when connecting to the EnterpriseDB Server. If this is not set, the user's default database will be used.

   Connect Using Standard Authentication

   To authenticate using standard authentication, set the following:

   • User: The user which will be used to authenticate with the EnterpriseDB server.
   • Password: The password which will be used to authenticate with the EnterpriseDB server.

   Connect Using SSL Authentication

   You can leverage SSL authentication to connect to EnterpriseDB data via a secure session. Configure the following connection properties to connect to data:

   • SSLClientCert: Set this to the name of the certificate store for the client certificate. Used in the case of 2-way SSL, where truststore and keystore are kept on both the client and server machines.
   • SSLClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
   • SSLClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
   • SSLClientCertType: The certificate type of the client store.
   • SSLServerCert: The certificate to be accepted from the server.
   
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, EnterpriseDB1).

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 ShipName, ShipCity "
    "FROM [EnterpriseDB1].[EnterpriseDB].[Orders] "
    "LIMIT 10"
)

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

Write Back to EnterpriseDB

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 [EnterpriseDB1].[EnterpriseDB].[Orders] (ShipName) "
    "VALUES (%(newvalue)s)",
    {"newvalue": "Example value"},
)
print(f"Rows inserted: {cur.rowcount}")

# Update existing records
cur.execute(
    "UPDATE [EnterpriseDB1].[EnterpriseDB].[Orders] "
    "SET ShipCity = %(newvalue)s "
    "WHERE ShipCountry = 'USA'",
    {"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 EnterpriseDB 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 EnterpriseDB data from Python through the CData Connect AI Python SDK. For more information on connecting to EnterpriseDB (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live EnterpriseDB data in Python.