Process & Analyze Zoom Data in Databricks (AWS)



Use CData, AWS, and Databricks to perform data engineering and data science on live Zoom Data.

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Zoom data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Zoom data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Zoom data. When you issue complex SQL queries to 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). Its built-in dynamic metadata querying allows you to work with and analyze Zoom data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Zoom data in Databricks, install the driver on your Databricks cluster.

  1. Navigate to your Databricks administration screen and select the target cluster.
  2. On the Libraries tab, click "Install New."
  3. Select "Upload" as the Library Source and "Jar" as the Library Type.
  4. Upload the JDBC JAR file (cdata.jdbc.api.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Zoom Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Zoom data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Zoom, and create a basic report.

Configure the Connection to Zoom

Connect to Zoom by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.api.APIDriver"
url = "jdbc:api:RTK=5246...;Profile=C:\profiles\Zoom.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Zoom JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.api.jar

Fill in the connection properties and copy the connection string to the clipboard.

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.

Load Zoom Data

Once you configure the connection, you can load Zoom data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "MeetingRegistrants") \
	.load ()

Display Zoom Data

Check the loaded Zoom data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

Analyze Zoom Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Zoom data for reporting, visualization, and analysis.

% sql

SELECT Id, JobTitle FROM SAMPLE_VIEW ORDER BY JobTitle DESC LIMIT 5

The data from Zoom is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData API Driver for JDBC and start working with your live Zoom data in Databricks. Reach out to our Support Team if you have any questions.

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Connect to live data from Zoom with the API Driver

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