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Complete read-write access to Sage 50 UK enables developers to search (Customers, Transactions, Invoices, Sales Receipts, etc.), update items, edit customers, and more, from any Java/J2EE application.

How to work with Sage 50 UK Data in Apache Spark using SQL

Access and process Sage 50 UK Data in Apache Spark using the CData JDBC Driver.

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Sage 50 UK, Spark can work with live Sage 50 UK data. This article describes how to connect to and query Sage 50 UK data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Sage 50 UK data due to optimized data processing built into the driver. 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 (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Sage 50 UK data using native data types.

Install the CData JDBC Driver for Sage 50 UK

Download the CData JDBC Driver for Sage 50 UK installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Sage 50 UK Data

  1. Open a terminal and start the Spark shell with the CData JDBC Driver for Sage 50 UK JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Sage 50 UK/lib/cdata.jdbc.sage50uk.jar
  2. With the shell running, you can connect to Sage 50 UK with a JDBC URL and use the SQL Context load() function to read a table.

    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.

    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.

    java -jar cdata.jdbc.sage50uk.jar

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

    Configure the connection to Sage 50 UK, using the connection string generated above.

    scala> val sage50uk_df ="jdbc").option("url", "jdbc:sage50uk:URL=http://your-server:5493/sdata/accounts50/GCRM/your-address;User=Manager;").option("dbtable","TradingAccounts").option("driver","cdata.jdbc.sage50uk.Sage50UKDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the Sage 50 UK data as a temporary table:

    scala> sage50uk_df.registerTable("tradingaccounts")
  5. Perform custom SQL queries against the Data using commands like the one below:

    scala> sage50uk_df.sqlContext.sql("SELECT Name, FinanceBalance FROM TradingAccounts WHERE TradingAccountUUID = c2ef66a5-a545-413b-9312-79a53caadbc4").collect.foreach(println)

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

Using the CData JDBC Driver for Sage 50 UK in Apache Spark, you are able to perform fast and complex analytics on Sage 50 UK data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.