Process & Analyze Azure Analysis Services Data in Databricks (AWS)

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Azure Analysis Services JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Azure Analysis Services.



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

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

Install the CData JDBC Driver in Databricks

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

Access Azure Analysis Services Data in your Notebook: Python

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

Configure the Connection to Azure Analysis Services

Connect to Azure Analysis Services by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL.

Step 1: Connection Information

driver = "cdata.jdbc.aas.AASDriver"
url = "jdbc:aas:URL=asazure://REGION.asazure.windows.net/server;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

java -jar cdata.jdbc.aas.jar

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

To connect to Azure Analysis Services, set the Url property to a valid server, for instance, asazure://southcentralus.asazure.windows.net/server, in addition to authenticating. Optionally, set Database to distinguish which Azure database on the server to connect to.

Azure Analysis Services uses the OAuth authentication standard. OAuth requires the authenticating user to interact with Azure Analysis Services using the browser. You can connect without setting any connection properties for your user credentials. See the Help documentation for more information.

Load Azure Analysis Services Data

Once you configure the connection, you can load Azure Analysis Services 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" , "Customer") \
	.load ()

Display Azure Analysis Services Data

Check the loaded Azure Analysis Services data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Country"))

Analyze Azure Analysis Services 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 Azure Analysis Services data for reporting, visualization, and analysis.

% sql

SELECT Country, Education FROM SAMPLE_VIEW ORDER BY Education DESC LIMIT 5

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