Getting Started with the CData Connect AI Python SDK for Sage X3 Cloud

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 Sage X3 Cloud data with standard Python database code. Because Connect AI provides the connectivity: you install one package, authenticate with a Personal Access Token, and query Sage X3 Cloud (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 Sage X3 Cloud in Connect AI, generating a Personal Access Token, installing the SDK, and reading (and, where supported, writing) live Sage X3 Cloud data.

Prerequisites

• An account in CData Connect AI
• Python 3.8 or higher
• An active Sage X3 Cloud account with valid credentials

Connect to Sage X3 Cloud 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 "Sage X3 Cloud" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Sage X3 Cloud.

   Sage X3 Cloud uses the OAuth 2.0 Client Credentials flow, and an X-API-Key is also required for API access. Set AuthScheme to OAuth and specify the following connection properties:

   • URL: The base URL of your Sage X3 Cloud instance.
   • OAuthAccessTokenUrl: The OAuth token endpoint (e.g., https://your-auth-domain/oauth/token).
   • OAuthClientId: Your OAuth application client ID.
   • OAuthClientSecret: Your OAuth application client secret.
   • Audience: The API audience value for the token request.
   • XAPIKey: The X-API-Key provided by your Sage X3 Cloud administrator.
   • Folder: The Sage X3 folder name (e.g., SEED). This folder is used as the default schema.
   • Folders (optional): A comma-separated list of Sage X3 folders (e.g., SEED,PERF). Each folder is exposed as a separate schema, so you can query across folders with the Schema.Table syntax.

   The driver obtains an access token with the Client Credentials flow and sends it with the X-API-Key on every API request. With InitiateOAuth set to GETANDREFRESH (the default), the driver acquires and refreshes the token automatically.

   
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, SageX3Cloud1).

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 BPCNUM, BPCNAM "
    "FROM [SageX3Cloud1].[SageX3Cloud].[BPCUSTOMER] "
    "LIMIT 10"
)

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

Sage X3 Cloud is a read-only source in Connect AI, so the SDK supports queries but not INSERT, UPDATE, or DELETE. Close the connection when you are finished:

conn.close()

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 Sage X3 Cloud 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 Sage X3 Cloud data from Python through the CData Connect AI Python SDK. For more information on connecting to Sage X3 Cloud (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live Sage X3 Cloud data in Python.