Process & Analyze Azure Data Catalog Data in Azure Databricks

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Azure Data Catalog JDBC Driver

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



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

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

Install the CData JDBC Driver in Azure

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

Connect to Azure Data Catalog from Databricks

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

Configure the Connection to Azure Data Catalog

Connect to Azure 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.azuredatacatalog.AzureDataCatalogDriver"
url = "jdbc:azuredatacatalog:RTK=5246...;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

java -jar cdata.jdbc.azuredatacatalog.jar

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

You can optionally set the following to read the different catalog data returned from Azure Data Catalog.

    CatalogName: Set this to the CatalogName associated with your Azure Data Catalog. To get your Catalog name, navigate to your Azure Portal home page > Data Catalog > Catalog Name

Connect Using OAuth Authentication

You must use OAuth to authenticate with Azure Data Catalog. OAuth requires the authenticating user to interact with Azure Data Catalog using the browser. For more information, refer to the OAuth section in the help documentation.

Load Azure Data Catalog Data

Once the connection is configured, you can load Azure 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" , "Tables") \
	.load ()

Display Azure Data Catalog Data

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

display (remote_table.select ("DslAddressDatabase"))

Analyze Azure 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 Azure Data Catalog data for analysis.

% sql

SELECT DslAddressDatabase, Type FROM Tables WHERE Name = 'FactProductInventory'

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