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Work with Azure Management Data in Apache Spark Using SQL

Access and process Azure Management Data in Apache Spark using the CData JDBC Driver.

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

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

Install the CData JDBC Driver for Azure Management

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

Start a Spark Shell and Connect to Azure Management Data

  1. Open a terminal and start the Spark shell with the CData JDBC Driver for Azure Management JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Azure Management/lib/cdata.jdbc.azuredatamanagement.jar
  2. With the shell running, you can connect to Azure Management with a JDBC URL and use the SQL Context load() function to read a table.

    Azure Data Management uses the OAuth 2 authentication standard. See the Getting Started section of the CData driver documentation for a guide.

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.azuredatamanagement.jar

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

    Configure the connection to Azure Management, using the connection string generated above.

    scala> val azuredatamanagement_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:azuredatamanagement:").option("dbtable","Subscriptions").option("driver","cdata.jdbc.azuredatamanagement.AzureDataManagementDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the Azure Management data as a temporary table:

    scala> azuredatamanagement_df.registerTable("subscriptions")
  5. Perform custom SQL queries against the Data using commands like the one below:

    scala> azuredatamanagement_df.sqlContext.sql("SELECT DisplayName, AuthorizationSource FROM Subscriptions WHERE SubscriptionId = fadc4-4cdaf-fadc4-4cdaf").collect.foreach(println)

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

Using the CData JDBC Driver for Azure Management in Apache Spark, you are able to perform fast and complex analytics on Azure Management data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 190+ CData JDBC Drivers and get started today.