How to connect and process Sage 200 Data from Azure Databricks



Use CData, Azure, and Databricks to perform data engineering and data science on live Sage 200 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 Sage 200 data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Sage 200 data in Databricks.

With built-in optimized data processing, the CData JDBC driver offers unmatched performance for interacting with live Sage 200 data. 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 client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Sage 200 data using native data types.

Install the CData JDBC Driver in Azure

To work with live Sage 200 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 "DBFS" as the Library Source and "Jar" as the Library Type.
  4. Upload the JDBC JAR file (cdata.jdbc.sage200.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Connect to Sage 200 from Databricks

With the JAR file installed, we are ready to work with live Sage 200 data in Databricks. Start by creating a new notebook in your workspace. Name the workbook, make sure Python is selected as the language (which should be by default), click on Connect and under General Compute select the cluster where you installed the JDBC driver (should be selected by default).

Configure the Connection to Sage 200

Connect to Sage 200 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.sage200.Sage200Driver"
url = "jdbc:sage200:RTK=5246...;SubscriptionKey=12345;Schema=StandardUK;InitiateOAuth=GETANDREFRESH"

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.

  • 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.

Load Sage 200 Data

Once the connection is configured, you can load Sage 200 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" , "Banks") \
	.load ()

Display Sage 200 Data

Check the loaded Sage 200 data by calling the display function.

display (remote_table.select ("Id"))

Analyze Sage 200 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 Sage 200 data for analysis.

result = spark.sql("SELECT Id, Code FROM SAMPLE_VIEW WHERE Code = '12345'")

The data from Sage 200 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 200 and start working with your live Sage 200 data in Azure Databricks. Reach out to our Support Team if you have any questions.

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