Getting Started with the CData Connect AI Python SDK for SAS xpt

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

Prerequisites

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

Connect to SAS xpt 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 "SAS xpt" from the Add Connection panel

3. Enter the necessary authentication properties to connect to SAS xpt.

   Connecting to Local SASXpt Files

   You can connect to local SASXpt file by setting the URI to a folder containing SASXpt files.

   Connecting to S3 data source

   You can connect to Amazon S3 source to read SASXpt files. Set the following properties to connect:

   • URI: Set this to the folder within your bucket that you would like to connect to.
   • AWSAccessKey: Set this to your AWS account access key.
   • AWSSecretKey: Set this to your AWS account secret key.
   • TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).

   Connecting to Azure Data Lake Storage Gen2

   You can connect to ADLS Gen2 to read SASXpt files. Set the following properties to connect:

   • URI: Set this to the name of the file system and the name of the folder which contacts your SASXpt files.
   • AzureAccount: Set this to the name of the Azure Data Lake storage account.
   • AzureAccessKey: Set this to our Azure DataLakeStore Gen 2 storage account access key.
   • TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).
   
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, SASXpt1).

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, Column1 "
    "FROM [SASXpt1].[SASXpt].[SampleTable_1] "
    "LIMIT 10"
)

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

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