How to work with YouTube Analytics Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for YouTube Analytics, Spark can work with live YouTube Analytics data. This article describes how to connect to and query YouTube Analytics data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live YouTube Analytics data due to optimized data processing built into the driver. 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 (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze YouTube Analytics data using native data types.

Install the CData JDBC Driver for YouTube Analytics

Download the CData JDBC Driver for YouTube Analytics installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to YouTube Analytics Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for YouTube Analytics JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for YouTube Analytics/lib/cdata.jdbc.youtubeanalytics.jar
   

2. With the shell running, you can connect to YouTube Analytics with a JDBC URL and use the SQL Context load() function to read a table.

   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.

   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.

   java -jar cdata.jdbc.youtubeanalytics.jar
   

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

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to YouTube Analytics, using the connection string generated above.

   scala> val youtubeanalytics_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:youtubeanalytics:ContentOwnerId=MyContentOwnerId;ChannelId=MyChannelId;InitiateOAuth=GETANDREFRESH;").option("dbtable","Groups").option("driver","cdata.jdbc.youtubeanalytics.YouTubeAnalyticsDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the YouTube Analytics data as a temporary table:

   scala> youtubeanalytics_df.registerTable("groups")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> youtubeanalytics_df.sqlContext.sql("SELECT Snippet_Title, ContentDetails_ItemCount FROM Groups WHERE Mine = True").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for YouTube Analytics in Apache Spark, you are able to perform fast and complex analytics on YouTube Analytics data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.