How to connect and process YouTube Analytics Data from Azure Databricks

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 YouTube Analytics data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live YouTube Analytics data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live YouTube Analytics data. When you issue complex SQL queries to YouTube Analytics, the driver pushes supported SQL operations, like filters and aggregations, directly to YouTube Analytics 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 YouTube Analytics data using native data types.

Install the CData JDBC Driver in Azure

To work with live YouTube Analytics data in Databricks, install the driver on your Azure 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.youtubeanalytics.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Connect to YouTube Analytics from Databricks

With the JAR file installed, we are ready to work with live YouTube Analytics 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 YouTube Analytics, and create a basic report.

Configure the Connection to YouTube Analytics

Connect to YouTube Analytics by referencing the class for the JDBC Driver 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.

driver = "cdata.jdbc.youtubeanalytics.YouTubeAnalyticsDriver"
url = "jdbc:youtubeanalytics:RTK=5246...;ContentOwnerId=MyContentOwnerId;ChannelId=MyChannelId;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

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

YouTube Analytics uses the OAuth authentication standard. You can use the embedded CData OAuth credentials or you can register an application with Google to obtain your own.

In addition to the OAuth values, to access YouTube Analytics data set ChannelId to the Id of a YouTube channel. You can obtain the channel Id in the advanced account settings for your channel. If not specified, the channel of the currently authenticated user will be used.

If you want to generate content owner reports, specify the ContentOwnerId property. This is the Id of the copyright holder for content in YouTube's rights management system. The content owner is the person or organization that claims videos and sets their monetization policy.

Load YouTube Analytics Data

Once the connection is configured, you can load YouTube Analytics data as a dataframe using the CData JDBC Driver and the connection information.

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

Display YouTube Analytics Data

Check the loaded YouTube Analytics data by calling the display function.

display (remote_table.select ("Snippet_Title"))

Analyze YouTube Analytics Data in Azure Databricks

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

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the YouTube Analytics data for analysis.

% sql

SELECT Snippet_Title, ContentDetails_ItemCount FROM Groups

The data from YouTube Analytics 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 JDBC Driver for YouTube Analytics and start working with your live YouTube Analytics data in Azure Databricks. Reach out to our Support Team if you have any questions.