How to connect and process Epicor Kinetic Data from Azure Databricks

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 Epicor Kinetic data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Epicor Kinetic data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live Epicor Kinetic data in Databricks, install the driver on your Azure 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.epicorkinetic.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Connect to Epicor Kinetic from Databricks

With the JAR file installed, we are ready to work with live Epicor Kinetic 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 Epicor Kinetic, and create a basic report.

Configure the Connection to Epicor Kinetic

Connect to Epicor Kinetic by referencing the class for the JDBC Driver 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.

driver = "cdata.jdbc.epicorkinetic.epicorKineticDriver"
url = "jdbc:epicorkinetic:RTK=5246...;Service=Erp.BO.CustomerSvc;ERPInstance=MyInstance;URL=https://myaccount.epicorsaas.com;User=username;Password=password;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Epicor Kinetic 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.

To successfully connect to your ERP instance, you must specify the following connection properties:

• Url:the URL of the server hosting your ERP instance. For example, https://myserver.EpicorSaaS.com
• ERPInstance: the name of your ERP instance.
• User: the username of your account.
• Password: the password of your account.
• Service: the service you want to retrieve data from. For example, BaqSvc.

In addition, you may also set the optional connection properties:

• ApiKey: An optional key that may be required for connection to some services depending on your account configuration.
• ApiVersion: Defaults to v1. May be set to v2 to use the newer Epicor API.
• Company: Required if you set the ApiVersion to v2.

Load Epicor Kinetic Data

Once the connection is configured, you can load Epicor Kinetic data as a dataframe using the CData JDBC Driver and the connection information.

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

Display Epicor Kinetic Data

Check the loaded Epicor Kinetic data by calling the display function.

display (remote_table.select ("CustNum"))

Analyze Epicor Kinetic Data in Azure Databricks

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

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the Epicor Kinetic data for analysis.

% sql

SELECT CustNum, Company FROM Customers WHERE CompanyName = 'CompanyName'

The data from Epicor Kinetic 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 Epicor Kinetic and start working with your live Epicor Kinetic data in Azure Databricks. Reach out to our Support Team if you have any questions.