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

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

Install the CData JDBC Driver for EmailOctopus

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

Start a Spark Shell and Connect to EmailOctopus Data

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

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

2. With the shell running, you can connect to EmailOctopus 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 EmailOctopus Profile on disk (e.g. C:\profiles\EmailOctopus.apip). Next, set the ProfileSettings connection property to the connection string for EmailOctopus (see below).

   EmailOctopus API Profile Settings

   Sign into your EmailOctopus account and navigate to Integrations & API > API > Your API Keys to generate or retrieve your API key.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the EmailOctopus 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 EmailOctopus, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\EmailOctopus.apip;ProfileSettings='APIKey=your_api_key';").option("dbtable","CampaignReportBounced").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 EmailOctopus data as a temporary table:

   scala> api_df.registerTable("campaignreportbounced")

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

   scala> api_df.sqlContext.sql("SELECT CampaignId, ContactId FROM CampaignReportBounced WHERE ContactStatus = bounced").collect.foreach(println)

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

   

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