How to Build an ETL App for PingOne Data in Python with CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK and the petl framework, you can build PingOne-connected applications and pipelines for extracting, transforming, and loading PingOne data. This article shows how to connect to Connect AI and use petl to extract, transform, and load PingOne data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so petl can read directly from the SDK connection with etl.fromdb. There is no driver to install per source: connect with a Personal Access Token and build your pipeline.

Connect to PingOne 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 "PingOne" from the Add Connection panel

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

   To connect to PingOne, configure these properties:

   • Region: The region where the data for your PingOne organization is being hosted.
   • AuthScheme: The type of authentication to use when connecting to PingOne.
   • Either WorkerAppEnvironmentId (required when using the default PingOne domain) or AuthorizationServerURL, configured as described below.

   Configuring WorkerAppEnvironmentId

   WorkerAppEnvironmentId is the ID of the PingOne environment in which your Worker application resides. This parameter is used only when the environment is using the default PingOne domain (auth.pingone). It is configured after you have created the custom OAuth application you will use to authenticate to PingOne, as described in Creating a Custom OAuth Application in the Help documentation.

   First, find the value for this property:

   1. From the home page of your PingOne organization, move to the navigation sidebar and click Environments.
   2. Find the environment in which you have created your custom OAuth/Worker application (usually Administrators), and click Manage Environment. The environment's home page displays.
   3. In the environment's home page navigation sidebar, click Applications.
   4. Find your OAuth or Worker application details in the list.
   5. Copy the value in the Environment ID field. It should look similar to:
      

      WorkerAppEnvironmentId='11e96fc7-aa4d-4a60-8196-9acf91424eca'

   Now set WorkerAppEnvironmentId to the value of the Environment ID field.

   Configuring AuthorizationServerURL

   AuthorizationServerURL is the base URL of the PingOne authorization server for the environment where your application is located. This property is only used when you have set up a custom domain for the environment, as described in the PingOne platform API documentation. See Custom Domains.

   Authenticating to PingOne with OAuth

   PingOne supports both OAuth and OAuthClient authentication. In addition to performing the configuration steps described above, there are two more steps to complete to support OAuth or OAuthCliet authentication:

   • Create and configure a custom OAuth application, as described in Creating a Custom OAuth Application in the Help documentation.
   • To ensure that the driver can access the entities in Data Model, confirm that you have configured the correct roles for the admin user/worker application you will be using, as described in Administrator Roles in the Help documentation.
   • Set the appropriate properties for the authscheme and authflow of your choice, as described in the following subsections.

   OAuth (Authorization Code grant)

   Set AuthScheme to OAuth.

   Desktop Applications

   Get and Refresh the OAuth Access Token

   After setting the following, you are ready to connect:

   • InitiateOAuth: GETANDREFRESH. To avoid the need to repeat the OAuth exchange and manually setting the OAuthAccessToken each time you connect, use InitiateOAuth.
   • OAuthClientId: The Client ID you obtained when you created your custom OAuth application.
   • OAuthClientSecret: The Client Secret you obtained when you created your custom OAuth application.
   • CallbackURL: The redirect URI you defined when you registered your custom OAuth application. For example: https://localhost:3333

   When you connect, the driver opens PingOne's OAuth endpoint in your default browser. Log in and grant permissions to the application. The driver then completes the OAuth process:

   1. The driver obtains an access token from PingOne and uses it to request data.
   2. The OAuth values are saved in the location specified in OAuthSettingsLocation, to be persisted across connections.

   The driver refreshes the access token automatically when it expires.

   For other OAuth methods, including Web Applications, Headless Machines, or Client Credentials Grant, refer to the Help documentation.

   
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 Required Modules

Install the SDK and the petl framework using the pip utility:

pip install cdata-connect-ai
pip install petl

Build an ETL App for PingOne Data in Python

Once the required modules are installed, you are ready to build the ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, import the modules and connect to Connect AI with your account email and PAT:

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Create a SQL Statement to Query PingOne

Use SQL to create a statement for querying PingOne. In this article, we read data from the [CData].[Administrators].Users entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, PingOne1).

sql = (
    "SELECT Id, Username "
    "FROM [PingOne1].[PingOne].[[CData].[Administrators].Users] "
    "WHERE EmployeeType = 'Contractor'"
)

Extract, Transform, and Load the PingOne Data

With a connection and query in hand, use petl to extract, transform, and load the PingOne data. In this example, we extract PingOne data, sort the data by the Username column, and load the data into a CSV file.

table1 = etl.fromdb(conn, sql)

table2 = etl.sort(table1, 'Username')

etl.tocsv(table2, '[cdata].[administrators].users_data.csv')

PingOne is a read-only source in Connect AI, so this pipeline can extract and transform PingOne data but not load rows back. Close the connection when the extract is complete:

conn.close()

With the CData Connect AI Python SDK, you can work with PingOne data just like you would with any database, including direct access to data in ETL packages like petl.

More Information and Free Trial

Now you can pipe live PingOne data through petl using the CData Connect AI Python SDK. For more information on connecting to PingOne (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live PingOne data in Python.

Full Source Code

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

sql = (
    "SELECT Id, Username "
    "FROM [PingOne1].[PingOne].[[CData].[Administrators].Users] "
    "WHERE EmployeeType = 'Contractor'"
)

table1 = etl.fromdb(conn, sql)

table2 = etl.sort(table1, 'Username')

etl.tocsv(table2, '[cdata].[administrators].users_data.csv')
conn.close()