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

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

Install the CData JDBC Driver for Outreach.io

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

Start a Spark Shell and Connect to Outreach.io Data

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

   You must use OAuth to authenticate with Outreach. Set the InitiateOAuth connection property to "GETANDREFRESH". For more information, refer to the OAuth section in the Help documentation.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.outreach.jar

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

   Configure the connection to Outreach.io, using the connection string generated above.

   scala> val outreach_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:outreach:").option("dbtable","Accounts").option("driver","cdata.jdbc.outreach.OutreachDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Outreach.io data as a temporary table:

   scala> outreach_df.registerTable("accounts")

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

   scala> outreach_df.sqlContext.sql("SELECT Name, NumberOfEmployees FROM Accounts WHERE Industry = Textiles").collect.foreach(println)

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

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