Process & Analyze Google Data Catalog Data in Azure Databricks

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Google Data Catalog JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Google Data Catalog.



Host the CData JDBC Driver for Google Data Catalog in Azure and use Databricks to perform data engineering and data science on live Google Data Catalog 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 Data Catalog data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Google Data Catalog data in Databricks.

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

Install the CData JDBC Driver in Azure

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

Connect to Google Data Catalog from Databricks

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

Configure the Connection to Google Data Catalog

Connect to Google Data Catalog 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.googledatacatalog.GoogleDataCatalogDriver"
url = "jdbc:googledatacatalog:RTK=5246...;ProjectId=YourProjectId;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

java -jar cdata.jdbc.googledatacatalog.jar

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

Google Data Catalog uses the OAuth authentication standard. Authorize access to Google APIs on behalf on individual users or on behalf of users in a domain.

Before connecting, specify the following to identify the organization and project you would like to connect to:

  • OrganizationId: The ID associated with the Google Cloud Platform organization resource you would like to connect to. Find this by navigating to the cloud console.

    Click the project selection drop-down, and select your organization from the list. Then, click More -> Settings. The organization ID is displayed on this page.

  • ProjectId: The ID associated with the Google Cloud Platform project resource you would like to connect to.

    Find this by navigating to the cloud console dashboard and selecting your project from the Select from drop-down. The project ID will be present in the Project info card.

When you connect, the OAuth endpoint opens in your default browser. Log in and grant permissions to the application to completes the OAuth process. For more information, refer to the OAuth section in the Help documentation.

Load Google Data Catalog Data

Once the connection is configured, you can load Google Data Catalog 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" , "Schemas") \
	.load ()

Display Google Data Catalog Data

Check the loaded Google Data Catalog data by calling the display function.

display (remote_table.select ("Type"))

Analyze Google Data Catalog 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 Data Catalog data for analysis.

% sql

SELECT Type, DatasetName FROM Schemas WHERE ProjectId = 'bigquery-public-data'

The data from Google Data Catalog 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 Data Catalog and start working with your live Google Data Catalog data in Apache NiFi. Reach out to our Support Team if you have any questions.