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

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

Install the CData JDBC Driver in Databricks

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

Access Google Calendar Data in your Notebook: Python

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

Configure the Connection to Google Calendar

Connect to Google Calendar 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.googlecalendar.GoogleCalendarDriver"
url = "jdbc:googlecalendar:RTK=5246...;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

You can connect to Google APIs on behalf of individual users or on behalf of a domain. Google uses the OAuth authentication standard. See the "Getting Started" section of the help documentation for a guide.

Load Google Calendar Data

Once you configure the connection, you can load Google Calendar 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" , "VacationCalendar") \
	.load ()

Display Google Calendar Data

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

Step 3: Checking the result

display (remote_table.select ("Summary"))

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

% sql

SELECT Summary, StartDateTime FROM SAMPLE_VIEW ORDER BY StartDateTime DESC LIMIT 5

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