How to work with Azure Analysis Services 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 Azure Analysis Services, Spark can work with live Azure Analysis Services data. This article describes how to connect to and query Azure Analysis Services data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Azure Analysis Services data due to optimized data processing built into the driver. 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 (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Azure Analysis Services data using native data types.

Install the CData JDBC Driver for Azure Analysis Services

Download the CData JDBC Driver for Azure Analysis Services installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Azure Analysis Services Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Azure Analysis Services JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Azure Analysis Services/lib/cdata.jdbc.aas.jar
   

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

   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.

   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.

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

   Configure the connection to Azure Analysis Services, using the connection string generated above.

   scala> val aas_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:aas:URL=asazure://REGION.asazure.windows.net/server;InitiateOAuth=GETANDREFRESH;").option("dbtable","Customer").option("driver","cdata.jdbc.aas.AASDriver").load()
   

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

4. Register the Azure Analysis Services data as a temporary table:

   scala> aas_df.registerTable("customer")

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

   scala> aas_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 Azure Analysis Services in Apache Spark, you are able to perform fast and complex analytics on Azure Analysis Services 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.