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

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

Install the CData JDBC Driver for Microsoft Exchange

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

Start a Spark Shell and Connect to Microsoft Exchange Data

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

   Specify the User and Password to connect to Exchange. Additionally, specify the address of the Exchange server you are connecting to and the Platform associated with the server.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.exchange.jar

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

   Configure the connection to Microsoft Exchange, using the connection string generated above.

   scala> val exchange_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:exchange:User='myUser@mydomain.onmicrosoft.com';Password='myPassword';Server='https://outlook.office365.com/EWS/Exchange.asmx';Platform='Exchange_Online';").option("dbtable","Contacts").option("driver","cdata.jdbc.exchange.ExchangeDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Microsoft Exchange data as a temporary table:

   scala> exchange_df.registerTable("contacts")

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

   scala> exchange_df.sqlContext.sql("SELECT GivenName, Size FROM Contacts WHERE BusinnessAddress_City = Raleigh").collect.foreach(println)

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

Using the CData JDBC Driver for Microsoft Exchange in Apache Spark, you are able to perform fast and complex analytics on Microsoft Exchange data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.