How to Build an ETL App for Auth0 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 API Driver for Python and the petl framework, you can build Auth0-connected applications and pipelines for extracting, transforming, and loading Auth0 data. This article shows how to connect to Auth0 with the CData Python Connector and use petl and pandas to extract, transform, and load Auth0 data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Auth0 data in Python. When you issue complex SQL queries from Auth0, the driver pushes supported SQL operations, like filters and aggregations, directly to Auth0 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Auth0 Data

Connecting to Auth0 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.

Start by setting the Profile connection property to the location of the Auth0 Profile on disk (e.g. C:\profiles\Auth0.apip). Next, set the ProfileSettings connection property to the connection string for Auth0 (see below).

Auth0 API Profile Settings

Create a Machine to Machine application in your Auth0 Dashboard under Applications, then authorize it for the Auth0 Management API with the required scopes.

After installing the CData Auth0 Connector, follow the procedure below to install the other required modules and start accessing Auth0 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 Auth0 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.api as mod

You can now connect with a connection string. Use the connect function for the CData Auth0 Connector to create a connection for working with Auth0 data.

cnxn = mod.connect("Profile=C:\profiles\Auth0.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Create a SQL Statement to Query Auth0

Use SQL to create a statement for querying Auth0. In this article, we read data from the ActionBindings entity.

sql = "SELECT Id, TriggerId FROM ActionBindings WHERE DisplayName = 'my-action-binding'"

Extract, Transform, and Load the Auth0 Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Auth0 data. In this example, we extract Auth0 data, sort the data by the TriggerId column, and load the data into a CSV file.

Loading Auth0 Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Auth0 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 API Driver for Python to start building Python apps and scripts with connectivity to Auth0 data. Reach out to our Support Team if you have any questions.

Full Source Code

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

cnxn = mod.connect("Profile=C:\profiles\Auth0.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

sql = "SELECT Id, TriggerId FROM ActionBindings WHERE DisplayName = 'my-action-binding'"

table1 = etl.fromdb(cnxn,sql)

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

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