Process & Analyze Reckon Data in Azure Databricks

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Reckon JDBC Driver

Complete read-write access to Reckon enables developers to search (Customers, Transactions, Invoices, Sales Receipts, etc.), update items, edit customers, and more, from any Java/J2EE application.



Host the CData JDBC Driver for Reckon in Azure and use Databricks to perform data engineering and data science on live Reckon data.

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

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

Install the CData JDBC Driver in Azure

To work with live Reckon 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.reckon.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Reckon\lib).

Connect to Reckon from Databricks

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

Configure the Connection to Reckon

Connect to Reckon 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.reckon.ReckonDriver"
url = "jdbc:reckon:RTK=5246...;User=RCUser;Password=RCUserPassword;URL=http://remotehost:8166;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.reckon.jar

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

When you are connecting to a local Reckon instance, you do not need to set any connection properties.

Requests to Reckon are made through the Remote Connector. The Remote Connector runs on the same machine as Reckon and accepts connections through a lightweight, embedded Web server. The server supports SSL/TLS, enabling users to connect securely from remote machines.

The first time you connect to your company file, you will need to authorize the Remote Connector with Reckon. See the "Getting Started" chapter of the help documentation for a guide.

Load Reckon Data

Once the connection is configured, you can load Reckon 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 Reckon Data

Check the loaded Reckon data by calling the display function.

display (remote_table.select ("Name"))

Analyze Reckon 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 Reckon data for analysis.

% sql

SELECT Name, CustomerBalance FROM Customers

The data from Reckon 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 Reckon and start working with your live Reckon data in Apache NiFi. Reach out to our Support Team if you have any questions.