Process & Analyze Google Analytics Data in Databricks (AWS)

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

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

Install the CData JDBC Driver in Databricks

To work with live Google Analytics 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.googleanalytics.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Google Analytics Data in your Notebook: Python

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

Configure the Connection to Google Analytics

Connect to Google Analytics 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.googleanalytics.GoogleAnalyticsDriver"
url = "jdbc:googleanalytics:RTK=5246...;Profile=MyProfile;InitiateOAuth=GETANDREFRESH"

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.

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

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, you will need to 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.

Load Google Analytics Data

Once you configure the connection, you can load Google Analytics 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" , "Traffic") \
	.load ()

Display Google Analytics Data

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

Step 3: Checking the result

display (remote_table.select ("Browser"))

Analyze Google Analytics 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 Google Analytics data for reporting, visualization, and analysis.

% sql

SELECT Browser, Sessions FROM SAMPLE_VIEW ORDER BY Sessions DESC LIMIT 5

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