How to connect and process Google Cloud Storage 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 Google Cloud Storage data. This article explains how to host the CData JDBC Driver in Azure, as well as connect to and process live Google Cloud Storage data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live Google Cloud Storage data in Databricks, install the driver through Azure Data Lake Storage (ADLS). (Please note that the method of connecting through DBFS, which previous versions of this article described, has been deprecated, but has not published an end-of-life.)

1. Upload the JDBC JAR file to a blob container of your choice (i.e. "jdbcjars" container of the "databrickslibraries" storage account).
2. Fetch the Account Key from the storage account by expanding "Security + networking" and clicking on "Access Keys". Show and copy whichever of the two keys you wish to use. 👁 Get Access Key
   
3. Get the JDBC JAR file's URL by navigating to Containers, opening the specific container storing the JAR, and selecting the entry for the JDBC JAR file. This should open the file's details, where there should be a convenient button to copy the URL button to clipboard. This value will look similar to the below, though the "blob" component may vary depending on storage account type:

   https://databrickslibraries.blob.core.windows.net/jdbcjars/cdata.jdbc.salesforce.jar

   👁 Get JAR URL
   
4. In the Configuration tab of your Databricks cluster, click on the Edit button and expand "Advanced options". From there, add the following Spark option (derived from the JAR URL's domain name) with your copied Account key as its value and click Confirm: spark.hadoop.fs.azure.account.key.databrickslibraries.blob.core.windows.net 👁 Apply Account Key
   
5. In the Libraries tab of your Databricks cluster, click on "Install new", and select the ADLS option. Specify the ABFSS URL for the driver JAR (also derived from the JAR URL's domain name), and click Install. The ABFSS URL should resemble the below:

   abfss://[email protected]/cdata.jdbc.salesforce.jar

   👁 Install ADLS Library
   

Connect to Google Cloud Storage from Databricks

With the JAR file installed, we are ready to work with live Google Cloud Storage data in Databricks. Start by creating a new notebook in your workspace. Name the workbook, make sure Python is selected as the language (which should be by default), click on Connect and under General Compute select the cluster where you installed the JDBC driver (should be selected by default).

👁 Attaching to an existing compute resource

Configure the Connection to Google Cloud Storage

Connect to Google Cloud Storage 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 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.googlecloudstorage.GoogleCloudStorageDriver"
url = "jdbc:googlecloudstorage:RTK=5246...;ProjectId='project1';InitiateOAuth=GETANDREFRESH;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.googlecloudstorage.jar

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

Authenticate with a User Account

You can connect without setting any connection properties for your user credentials. After setting InitiateOAuth to GETANDREFRESH, you are ready to connect.

When you connect, the Google Cloud Storage OAuth endpoint opens in your default browser. Log in and grant permissions, then the OAuth process completes

Authenticate with a Service Account

Service accounts have silent authentication, without user authentication in the browser. You can also use a service account to delegate enterprise-wide access scopes.

You need to create an OAuth application in this flow. See the Help documentation for more information. After setting the following connection properties, you are ready to connect:

• InitiateOAuth: Set this to GETANDREFRESH.
• OAuthJWTCertType: Set this to "PFXFILE".
• OAuthJWTCert: Set this to the path to the .p12 file you generated.
• OAuthJWTCertPassword: Set this to the password of the .p12 file.
• OAuthJWTCertSubject: Set this to "*" to pick the first certificate in the certificate store.
• OAuthJWTIssuer: In the service accounts section, click Manage Service Accounts and set this field to the email address displayed in the service account Id field.
• OAuthJWTSubject: Set this to your enterprise Id if your subject type is set to "enterprise" or your app user Id if your subject type is set to "user".
• ProjectId: Set this to the Id of the project you want to connect to.

The OAuth flow for a service account then completes.

👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)

Load Google Cloud Storage Data

Once the connection is configured, you can load Google Cloud Storage 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" , "Buckets") \
	.load ()

Display Google Cloud Storage Data

Check the loaded Google Cloud Storage data by calling the display function.

display (remote_table.select ("Name"))

👁 Displaying Google Cloud Storage Data

Analyze Google Cloud Storage 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 Google Cloud Storage data for analysis.

result = spark.sql("SELECT Name, OwnerId FROM SAMPLE_VIEW WHERE Name = 'TestBucket'")

The data from Google Cloud Storage 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" )

👁 Displaying Google Cloud Storage Data

Download a free, 30-day trial of the CData JDBC Driver for Google Cloud Storage and start working with your live Google Cloud Storage data in Azure Databricks. Reach out to our Support Team if you have any questions.