Process & Analyze Gmail 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 Gmail data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Gmail data in Databricks.

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

Install the CData JDBC Driver in Databricks

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

Access Gmail Data in your Notebook: Python

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

Configure the Connection to Gmail

Connect to Gmail 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.gmail.GmailDriver"
url = "jdbc:gmail:RTK=5246...;User=username;Password=password;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Gmail 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.

There are two ways to authenticate to Gmail. Before selecting one, first ensure that you have enabled IMAP access in your Gmail account settings. See the "Connecting to Gmail" section under "Getting Started" in the installed documentation for a guide.

The User and Password properties, under the Authentication section, can be set to valid Gmail user credentials.

Alternatively, instead of providing the Password, you can use 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, you will need to provide the User. See the "Getting Started" chapter in the help documentation for a guide to using OAuth.

Load Gmail Data

Once you configure the connection, you can load Gmail 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" , "Inbox") \
	.load ()

Display Gmail Data

Check the loaded Gmail data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Subject"))

Analyze Gmail 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 Gmail data for reporting, visualization, and analysis.

% sql

SELECT Subject, Size FROM SAMPLE_VIEW ORDER BY Size DESC LIMIT 5

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