How to work with Office 365 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 Office 365, Spark can work with live Office 365 data. This article describes how to connect to and query Office 365 data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Office 365 data due to optimized data processing built into the driver. When you issue complex SQL queries to Office 365, the driver pushes supported SQL operations, like filters and aggregations, directly to Office 365 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 Office 365 data using native data types.

Install the CData JDBC Driver for Office 365

Download the CData JDBC Driver for Office 365 installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Office 365 Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Office 365 JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Office 365/lib/cdata.jdbc.office365.jar
   

2. With the shell running, you can connect to Office 365 with a JDBC URL and use the SQL Context load() function to read a table.

   Office 365 uses the OAuth authentication standard. To authenticate requests, obtain the OAuthClientId, OAuthClientSecret, and OAuthCallbackURL by registering an app with Office 365. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Office 365 JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.office365.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 Office 365, using the connection string generated above.

   scala> val office365_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:office365:OAuthClientId=MyApplicationId;OAuthClientSecret=MyAppKey;OAuthCallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;").option("dbtable","Files").option("driver","cdata.jdbc.office365.Office365Driver").load()
   

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

4. Register the Office 365 data as a temporary table:

   scala> office365_df.registerTable("files")

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

   scala> office365_df.sqlContext.sql("SELECT Name, Size FROM Files WHERE UserId = 54f34750-0d34-47c9-9949-9fac4791cddb").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 Office 365 in Apache Spark, you are able to perform fast and complex analytics on Office 365 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.