Process & Analyze Sage 50 UK 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 Sage 50 UK data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Sage 50 UK data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Sage 50 UK data. When you issue complex SQL queries to Sage 50 UK, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage 50 UK 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 Sage 50 UK data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Sage 50 UK 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.sage50uk.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Sage 50 UK Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Sage 50 UK 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 Sage 50 UK, and create a basic report.

Configure the Connection to Sage 50 UK

Connect to Sage 50 UK 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.sage50uk.Sage50UKDriver"
url = "jdbc:sage50uk:RTK=5246...;URL=http://your-server:5493/sdata/accounts50/GCRM/your-address;User=Manager;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Sage 50 UK 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.

Note: Only Sage 50 UK 2012 and above are supported.

The User and Password properties, under the Connection section, must be set to valid Sage 50 UK user credentials. These values will be the same used to log in to the Sage 50 UK software.

Additionally, the URL property, under the Connection section, will need to be set to the address of the company dataset desired. To obtain the address, do the following:

1. If you have not already done so, open the Sage 50 UK software.
2. Click Tools -> Internet Options.
3. Select the SData Settings tab.
4. Click the Details button next to Sage 50 Accounts. A window is displayed containing a list of company names along with the address to their corresponding datasets.
5. Set the URL property to the value in the address field next to the company desired.

Load Sage 50 UK Data

Once you configure the connection, you can load Sage 50 UK 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" , "TradingAccounts") \
	.load ()

Display Sage 50 UK Data

Check the loaded Sage 50 UK data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Name"))

Analyze Sage 50 UK 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 Sage 50 UK data for reporting, visualization, and analysis.

% sql

SELECT Name, FinanceBalance FROM SAMPLE_VIEW ORDER BY FinanceBalance DESC LIMIT 5

The data from Sage 50 UK 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 Sage 50 UK and start working with your live Sage 50 UK data in Databricks. Reach out to our Support Team if you have any questions.