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

The CData JDBC Driver offers unmatched performance for interacting with live Azure Active Directory data due to optimized data processing built into the driver. When you issue complex SQL queries to Azure Active Directory, the driver pushes supported SQL operations, like filters and aggregations, directly to Azure Active Directory 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 Active Directory data using native data types.

Install the CData JDBC Driver for Azure Active Directory

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

Start a Spark Shell and Connect to Azure Active Directory Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Azure Active Directory/lib/cdata.jdbc.azuread.jar
   

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

   Azure Active Directory uses the OAuth authentication standard. To authenticate using OAuth, create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the OAuth section in the Help documentation for an authentication guide.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.azuread.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 Active Directory, using the connection string generated above.

   scala> val azuread_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:azuread:OAuthClientId=MyApplicationId;OAuthClientSecret=MySecretKey;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;").option("dbtable","Domains").option("driver","cdata.jdbc.azuread.AzureADDriver").load()
   

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

4. Register the Azure Active Directory data as a temporary table:

   scala> azuread_df.registerTable("domains")

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

   scala> azuread_df.sqlContext.sql("SELECT id, availabilityStatus FROM Domains WHERE isVerified = TRUE").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 Active Directory in Apache Spark, you are able to perform fast and complex analytics on Azure Active Directory 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.