Process & Analyze Google Sheets Data in Databricks (AWS)

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Google Sheets JDBC Driver

Easily connect Java applications with real-time data from spreadsheets stored in Google Docs. Use Google Sheets to manage the data that powers your applications.



Host the CData JDBC Driver for Google Sheets in AWS and use Databricks to perform data engineering and data science on live Google Sheets data.

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

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

Install the CData JDBC Driver in Databricks

To work with live Google Sheets 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.googlesheets.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Google Sheets\lib).

Access Google Sheets Data in your Notebook: Python

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

Configure the Connection to Google Sheets

Connect to Google Sheets by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL.

Step 1: Connection Information

driver = "cdata.jdbc.googlesheets.GoogleSheetsDriver"
url = "jdbc:googlesheets:Spreadsheet=MySheet;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

java -jar cdata.jdbc.googlesheets.jar

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

You can connect to a spreadsheet by providing authentication to Google and then setting the Spreadsheet connection property to the name or feed link of the spreadsheet. If you want to view a list of information about the spreadsheets in your Google Drive, execute a query to the Spreadsheets view after you authenticate.

ClientLogin (username/password authentication) has been officially deprecated since April 20, 2012 and is now no longer available. Instead, use the OAuth 2.0 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.

See the Getting Started chapter in the help documentation to connect to Google Sheets from different types of accounts: Google accounts, Google Apps accounts, and accounts using two-step verification.

Load Google Sheets Data

Once you configure the connection, you can load Google Sheets 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" , "Orders") \
	.load ()

Display Google Sheets Data

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

Step 3: Checking the result

display (remote_table.select ("Shipcountry"))

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

% sql

SELECT Shipcountry, OrderPrice FROM SAMPLE_VIEW ORDER BY OrderPrice DESC LIMIT 5

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