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

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

Install the CData JDBC Driver for Intercom

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

Start a Spark Shell and Connect to Intercom Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Intercom/lib/cdata.jdbc.api.jar
   

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

   Start by setting the Profile connection property to the location of the Intercom Profile on disk (e.g. C:\profiles\Intercom.apip). Next, set the ProfileSettings connection property to the connection string for Intercom (see below).

   Intercom API Profile Settings

   In the Intercom Developer Hub, go to Configure > Authentication and select your workspace to obtain an Access Token. For OAuth, register an app and retrieve the Client ID and Secret from the app's Basic Information page.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.api.jar
   

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

   

   Configure the connection to Intercom, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Intercom.apip;ProfileSettings='APIKey=your_access_token';").option("dbtable","Admins").option("driver","cdata.jdbc.api.APIDriver").load()
   

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

4. Register the Intercom data as a temporary table:

   scala> api_df.registerTable("admins")

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

   scala> api_df.sqlContext.sql("SELECT Id, Type FROM Admins WHERE Email = [email protected]").collect.foreach(println)

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

   

Using the CData JDBC Driver for Intercom in Apache Spark, you are able to perform fast and complex analytics on Intercom 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.