How to work with Google 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 Google Analytics, Spark can work with live Google Analytics data. This article describes how to connect to and query Google Analytics data from a Spark shell.

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

Install the CData JDBC Driver for Google Analytics

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

Start a Spark Shell and Connect to Google Analytics Data

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

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

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

   Google uses the OAuth authentication standard. To access Google APIs on behalf on individual users, you can use the embedded credentials or you can register your own OAuth app.

   OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, register an application to obtain the OAuth JWT values.

   In addition to the OAuth values, set Profile to the profile you want to connect to. This can be set to either the Id or website URL for the Profile. If not specified, the first Profile returned will be used.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.googleanalytics.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 Google Analytics, using the connection string generated above.

   scala> val googleanalytics_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:googleanalytics:Profile=MyProfile;InitiateOAuth=GETANDREFRESH;").option("dbtable","Traffic").option("driver","cdata.jdbc.googleanalytics.GoogleAnalyticsDriver").load()
   

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

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

   scala> googleanalytics_df.registerTable("traffic")

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

   scala> googleanalytics_df.sqlContext.sql("SELECT Browser, Sessions FROM Traffic WHERE Transactions = 0").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 Google Analytics in Apache Spark, you are able to perform fast and complex analytics on Google 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.