How to work with Power BI XMLA Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Power BI XMLA, Spark can work with live Power BI XMLA data. This article describes how to connect to and query Power BI XMLA data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Power BI XMLA data due to optimized data processing built into the driver. When you issue complex SQL queries to Power BI XMLA, the driver pushes supported SQL operations, like filters and aggregations, directly to Power BI XMLA and utilizes the embedded SQL engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Power BI XMLA data using native data types.

Install the CData JDBC Driver for Power BI XMLA

Download the CData JDBC Driver for Power BI XMLA installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Power BI XMLA Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Power BI XMLA JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Power BI XMLA/lib/cdata.jdbc.powerbixmla.jar
   

2. With the shell running, you can connect to Power BI XMLA with a JDBC URL and use the SQL Context load() function to read a table.

   By default, use Entra ID (formerly Azure AD) to connect to Microsoft Power BI XMLA. Entra ID (formerly Azure AD) is Microsoft's multi-tenant, cloud-based directory and identity management service. It is user-based authentication that requires that you set AuthScheme to EntraID (formerly AzureAD).

   For more information on other authentication schemes, refer to the Help documentation.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.powerbixmla.jar
   

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

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

   Configure the connection to Power BI XMLA, using the connection string generated above.

   scala> val powerbixmla_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:powerbixmla:AuthScheme=EntraID;InitiateOAuth=GETANDREFRESH;").option("dbtable","Customer").option("driver","cdata.jdbc.powerbixmla.PowerBIXMLADriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Power BI XMLA data as a temporary table:

   scala> powerbixmla_df.registerTable("customer")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> powerbixmla_df.sqlContext.sql("SELECT Country, Education FROM Customer WHERE Country = Australia").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for Power BI XMLA in Apache Spark, you are able to perform fast and complex analytics on Power BI XMLA data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.