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

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

Install the CData JDBC Driver for Xero WorkflowMax

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

Start a Spark Shell and Connect to Xero WorkflowMax Data

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

   To connect to the WorkflowMax API, obtain an APIKey and AccountKey from Xero. This can only be done by contacting Xero support (https://www.workflowmax.com/contact-us).

   After obtaining an API Key and Account Key, set the values in the APIKey and AccountKey connection properties. Once these are set, you are ready to connect.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.xeroworkflowmax.jar

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

   Configure the connection to Xero WorkflowMax, using the connection string generated above.

   scala> val xeroworkflowmax_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:xeroworkflowmax:APIKey=myApiKey;AccountKey=myAccountKey;").option("dbtable","Clients").option("driver","cdata.jdbc.xeroworkflowmax.XeroWorkflowMaxDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Xero WorkflowMax data as a temporary table:

   scala> xeroworkflowmax_df.registerTable("clients")

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

   scala> xeroworkflowmax_df.sqlContext.sql("SELECT Id, Name FROM Clients WHERE Name = Cynthia").collect.foreach(println)

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

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