How to Build an ETL App for Okta 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 Okta-connected applications and pipelines for extracting, transforming, and loading Okta data. This article shows how to connect to Connect AI and use petl to extract, transform, and load Okta 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 Okta 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 "Okta" from the Add Connection panel

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

   To connect to Okta, set the Domain connection string property to your Okta domain.

   You will use OAuth to authenticate with Okta, so you need to create a custom OAuth application.

   Creating a Custom OAuth Application

   From your Okta account:

   1. Sign in to your Okta developer edition organization with your administrator account.
   2. In the Admin Console, go to Applications > Applications.
   3. Click Create App Integration.
   4. For the Sign-in method, select OIDC - OpenID Connect.
   5. For Application type, choose Web Application.
   6. Enter a name for your custom application.
   7. Set the Grant Type to Authorization Code. If you want the token to be automatically refreshed, also check Refresh Token.
   8. Set the callback URL:
      • For desktop applications and headless machines, use http://localhost:33333 or another port number of your choice. The URI you set here becomes the CallbackURL property.
      • For web applications, set the callback URL to a trusted redirect URL. This URL is the web location the user returns to with the token that verifies that your application has been granted access.
   9. In the Assignments section, either select Limit access to selected groups and add a group, or skip group assignment for now.
   10. Save the OAuth application.
   11. The application's Client Id and Client Secret are displayed on the application's General tab. Record these for future use. You will use the Client Id to set the OAuthClientId and the Client Secret to set the OAuthClientSecret.
   12. Check the Assignments tab to confirm that all users who must access the application are assigned to the application.
   13. On the Okta API Scopes tab, select the scopes you wish to grant to the OAuth application. These scopes determine the data that the app has permission to read, so a scope for a particular view must be granted for the driver to have permission to query that view. To confirm the scopes required for each view, see the view-specific pages in Data Model < Views in 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 Okta 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 Okta

Use SQL to create a statement for querying Okta. In this article, we read data from the Users entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, Okta1).

sql = (
    "SELECT Id, ProfileFirstName "
    "FROM [Okta1].[Okta].[Users] "
    "WHERE Status = 'Active'"
)

Extract, Transform, and Load the Okta Data

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

table1 = etl.fromdb(conn, sql)

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

etl.tocsv(table2, 'users_data.csv')

Okta is a read-only source in Connect AI, so this pipeline can extract and transform Okta 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 Okta 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 Okta data through petl using the CData Connect AI Python SDK. For more information on connecting to Okta (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live Okta 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, ProfileFirstName "
    "FROM [Okta1].[Okta].[Users] "
    "WHERE Status = 'Active'"
)

table1 = etl.fromdb(conn, sql)

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

etl.tocsv(table2, 'users_data.csv')
conn.close()