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

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

Connecting to Zoom Data

Connecting to Zoom 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 Zoom Profile on disk (e.g. C:\profiles\Zoom.apip). Next, set the ProfileSettings connection property to the connection string for Zoom (see below).

Zoom API Profile Settings

To authenticate to Zoom, you can use the OAuth standard to connect to your own data or to allow other users to connect to their data.

First you will need to create an OAuth app. To do so, navigate to https://marketplace.zoom.us/develop/create and click Create under the OAuth section. Select whether or not the app will be for individual users or for the entire account, and uncheck the box to publish the app. Give the app a name and click Create. You will then be given your Client Secret and Client ID

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
• OAuthClientID: Set this to the OAuth Client ID that is specified in your app settings.
• OAuthClientSecret: Set this to the OAuth Client Secret that is specified in your app settings.
• CallbackURL: Set this to the Redirect URI you specified in your app settings.

After installing the CData Zoom Connector, follow the procedure below to install the other required modules and start accessing Zoom 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 Zoom 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 Zoom Connector to create a connection for working with Zoom data.

cnxn = mod.connect("Profile=C:\profiles\Zoom.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Zoom

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

sql = "SELECT Id, JobTitle FROM MeetingRegistrants WHERE State = 'NC'"

Extract, Transform, and Load the Zoom Data

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

Loading Zoom Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Zoom 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 Zoom 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\Zoom.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, JobTitle FROM MeetingRegistrants WHERE State = 'NC'"

table1 = etl.fromdb(cnxn,sql)

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

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