How to connect and process Unbounce 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 Unbounce data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Unbounce data in Databricks.

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

Install the CData JDBC Driver in Azure

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

Connect to Unbounce from Databricks

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

Configure the Connection to Unbounce

Connect to Unbounce 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.api.APIDriver"
url = "jdbc:api:RTK=5246...;Profile=C:\profiles\Unbounce.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 Unbounce 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.

Start by setting the Profile connection property to the location of the Unbounce Profile on disk (e.g. C:\profiles\Unbounce.apip). Next, set the ProfileSettings connection property to the connection string for Unbounce (see below).

Unbounce API Profile Settings

Unbounce uses OAuth to authenticate to your data.

In order to authenticate to Unbounce, you will first need to register an OAuth application. To do so, go to https://developer.unbounce.com/getting_started/ and complete the Register OAuth Application form.

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 Client Id that is specified in your app settings.
• OAuthClientSecret: Set this to Client Secret that is specified in your app settings.
• CallbackURL: Set this to the Redirect URI you specified in your app settings.

Load Unbounce Data

Once the connection is configured, you can load Unbounce 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" , "Tags") \
	.load ()

Display Unbounce Data

Check the loaded Unbounce data by calling the display function.

display (remote_table.select ("Id"))

Analyze Unbounce 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 Unbounce data for analysis.

% sql

SELECT Id, Name FROM Tags WHERE State = 'active'

The data from Unbounce 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 Unbounce data in Azure Databricks. Reach out to our Support Team if you have any questions.