Process & Analyze Wave Financial Data in Databricks (AWS)

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Wave Financial data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Wave Financial data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Wave Financial data. 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 client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Wave Financial data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Wave Financial data in Databricks, install the driver on your Databricks cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.wavefinancial.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Wave Financial Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Wave Financial data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Wave Financial, and create a basic report.

Configure the Connection to Wave Financial

Connect to Wave Financial by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.wavefinancial.WaveFinancialDriver"
url = "jdbc:wavefinancial:RTK=5246...;InitiateOAuth=GETANDREFRESH"

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.

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

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.

Load Wave Financial Data

Once you configure the connection, you can load Wave Financial data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "Invoices") \
	.load ()

Display Wave Financial Data

Check the loaded Wave Financial data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

Analyze Wave Financial Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Wave Financial data for reporting, visualization, and analysis.

% sql

SELECT Id, DueDate FROM SAMPLE_VIEW ORDER BY DueDate DESC LIMIT 5

The data from Wave Financial is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for Wave Financial and start working with your live Wave Financial data in Databricks. Reach out to our Support Team if you have any questions.