How to work with Sage 200 Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Sage 200, Spark can work with live Sage 200 data. This article describes how to connect to and query Sage 200 data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Sage 200 data due to optimized data processing built into the driver. When you issue complex SQL queries to Sage 200, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage 200 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 200 data using native data types.

Install the CData JDBC Driver for Sage 200

Download the CData JDBC Driver for Sage 200 installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Sage 200 Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Sage 200 JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Sage 200/lib/cdata.jdbc.sage200.jar
   

2. With the shell running, you can connect to Sage 200 with a JDBC URL and use the SQL Context load() function to read a table.
   • Schema: Determines which Sage 200 edition you are connecting to. Specify either StandardUK or ProfessionalUK.
   • Subscription Key: Provides access to the APIs that are used to establish a connection. You will first need to log into the Sage 200 API website and subscribe to the API edition that matches your account. You can do so here: https://developer.columbus.sage.com/docs/services/api/uk. Afterwards, the subscription key may be found in your profile after logging into Sage 200.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Sage 200 JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.sage200.jar
   

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

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to Sage 200, using the connection string generated above.

   scala> val sage200_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:sage200:SubscriptionKey=12345;Schema=StandardUK;InitiateOAuth=GETANDREFRESH;").option("dbtable","Banks").option("driver","cdata.jdbc.sage200.Sage200Driver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Sage 200 data as a temporary table:

   scala> sage200_df.registerTable("banks")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> sage200_df.sqlContext.sql("SELECT Id, Code FROM Banks WHERE Code = 12345").collect.foreach(println)

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

   👁 Data in Apache Spark (Salesforce is shown)
   

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