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

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for PingOne 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 PingOne with the CData Python Connector and use petl and pandas to extract, transform, and load PingOne data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live PingOne data in Python. When you issue complex SQL queries from PingOne, the driver pushes supported SQL operations, like filters and aggregations, directly to PingOne and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to PingOne Data

Connecting to PingOne data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

To connect to PingOne, configure these properties:

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

Configuring 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 to the value of the Environment ID field.

Configuring 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 to OAuth.

Desktop Applications

Get and Refresh the OAuth Access Token

After setting the following, you are ready to connect:

• : GETANDREFRESH. To avoid the need to repeat the OAuth exchange and manually setting the each time you connect, use .
• : The Client ID you obtained when you created your custom OAuth application.
• : The Client Secret you obtained when you created your custom OAuth application.
• : 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 , 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.

After installing the CData PingOne Connector, follow the procedure below to install the other required modules and start accessing PingOne through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for PingOne Data in Python

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

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.pingone as mod

You can now connect with a connection string. Use the connect function for the CData PingOne Connector to create a connection for working with PingOne data.

cnxn = mod.connect("AuthScheme=OAuth;WorkerAppEnvironmentId=eebc33a8-xxxx-4f3a-yyyy-d3e5262fd49e;Region=NA;OAuthClientId=client_id;OAuthClientSecret=client_secret;InitiateOAuth=GETANDREFRESH;")

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.

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

Extract, Transform, and Load the PingOne Data

With the query results stored in a DataFrame, we can 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.

Loading PingOne Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for PingOne to start building Python apps and scripts with connectivity to PingOne data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.pingone as mod

cnxn = mod.connect("AuthScheme=OAuth;WorkerAppEnvironmentId=eebc33a8-xxxx-4f3a-yyyy-d3e5262fd49e;Region=NA;OAuthClientId=client_id;OAuthClientSecret=client_secret;InitiateOAuth=GETANDREFRESH;")

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

table1 = etl.fromdb(cnxn,sql)

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

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