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

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

Install the CData JDBC Driver for Wave Financial

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

Start a Spark Shell and Connect to Wave Financial Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Wave Financial/lib/cdata.jdbc.wavefinancial.jar
   

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

   Connect using the API Token

   You can connect to Wave Financial by specifying the APIToken You can obtain an API Token using the following steps:

   1. Log in to your Wave account and navigate to "Manage Applications" in the left pane.
   2. Select the application that you would like to create a token for. You may need to create an application first.
   3. Click the "Create token" button to generate an APIToken.

   Connect using OAuth

   If you wish, you can connect using the embedded OAuth credentials. See the Help documentation for more information.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.wavefinancial.jar
   

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

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to Wave Financial, using the connection string generated above.

   scala> val wavefinancial_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:wavefinancial:InitiateOAuth=GETANDREFRESH;").option("dbtable","Invoices").option("driver","cdata.jdbc.wavefinancial.WaveFinancialDriver").load()
   

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

4. Register the Wave Financial data as a temporary table:

   scala> wavefinancial_df.registerTable("invoices")

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

   scala> wavefinancial_df.sqlContext.sql("SELECT Id, DueDate FROM Invoices WHERE Status = SENT").collect.foreach(println)

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

   👁 Data in Apache Spark (Salesforce is shown)
   

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