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

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

Install the CData JDBC Driver for Azure Table

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

Start a Spark Shell and Connect to Azure Table Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Azure Table/lib/cdata.jdbc.azuretables.jar
   

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

   Specify your AccessKey and your Account to connect. Set the Account property to the Storage Account Name and set AccessKey to one of the Access Keys. Either the Primary or Secondary Access Keys can be used. To obtain these values, navigate to the Storage Accounts blade in the Azure portal. You can obtain the access key by selecting your account and clicking Access Keys in the Settings section.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.azuretables.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 Table, using the connection string generated above.

   scala> val azuretables_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:azuretables:AccessKey=myAccessKey;Account=myAccountName;").option("dbtable","NorthwindProducts").option("driver","cdata.jdbc.azuretables.AzureTablesDriver").load()
   

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

4. Register the Azure Table data as a temporary table:

   scala> azuretables_df.registerTable("northwindproducts")

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

   scala> azuretables_df.sqlContext.sql("SELECT Name, Price FROM NorthwindProducts WHERE ShipCity = New York").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 Table in Apache Spark, you are able to perform fast and complex analytics on Azure Table 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.