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

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

Install the CData JDBC Driver for Klaviyo

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

Start a Spark Shell and Connect to Klaviyo Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Klaviyo JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Klaviyo/lib/cdata.jdbc.api.jar
2. With the shell running, you can connect to Klaviyo 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 Klaviyo Profile on disk (e.g. C:\profiles\Klaviyo.apip). Next, set the ProfileSettings connection property to the connection string for Klaviyo (see below).

   Klaviyo API Profile Settings

   To authenticate to Klaviyo, you will needto provide an API Key. You can generate or view your API keys under 'My Account' > 'Setting' > 'API Key' > 'Create API Key'. Set the API Key to your Klaviyo Key in the ProfileSettings connection property.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("campaigns")

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

   scala> api_df.sqlContext.sql("SELECT Id, Name FROM Campaigns WHERE Status = draft").collect.foreach(println)

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

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