Build Sage 300-Connected ETL Processes in Google Data Fusion

Google Data Fusion allows users to perform self-service data integration to consolidate disparate data. Uploading the CData JDBC Driver for Sage 300 enables users to access live Sage 300 data from within their Google Data Fusion pipelines. While the CData JDBC Driver enables piping Sage 300 data to any data source natively supported in Google Data Fusion, this article walks through piping data from Sage 300 to Google BigQuery,

Upload the CData JDBC Driver for Sage 300 to Google Data Fusion

Upload the CData JDBC Driver for Sage 300 to your Google Data Fusion instance to work with live Sage 300 data. Due to the naming restrictions for JDBC drivers in Google Data Fusion, create a copy or rename the JAR file to match the following format driver-version.jar. For example: cdatasage300-2020.jar

1. Open your Google Data Fusion instance
2. Click the to add an entity and upload a driver
3. On the "Upload driver" tab, drag or browse to the renamed JAR file.
4. On the "Driver configuration" tab:
   • Name: Create a name for the driver (cdata.jdbc.sage300) and make note of the name
   • Class name: Set the JDBC class name: (cdata.jdbc.sage300.Sage300Driver)
5. Click "Finish"

Connect to Sage 300 Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live Sage 300 data in Google Data Fusion Pipelines.

1. Navigate to the Pipeline Studio to create a new Pipeline
2. From the "Source" options, click "Database" to add a source for the JDBC Driver
3. Click "Properties" on the Database source to edit the properties

   NOTE: To use the JDBC Driver in Google Data Fusion, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

   • Set the Label
   • Set Reference Name to a value for any future references (i.e.: cdata-sage300)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for Sage 300. For example:
     
     jdbc:sage300:RTK=5246...;User=SAMPLE;Password=password;URL=http://127.0.0.1/Sage300WebApi/v1/-/;Company=SAMINC;

     Sage 300 requires some initial setup in order to communicate over the Sage 300 Web API.

     • Set up the security groups for the Sage 300 user. Give the Sage 300 user access to the option under Security Groups (per each module required).
     • Edit both web.config files in the /Online/Web and /Online/WebApi folders; change the key AllowWebApiAccessForAdmin to true. Restart the webAPI app-pool for the settings to take.
     • Once the user access is configured, click https://server/Sage300WebApi/ to ensure access to the web API.

     Authenticate to Sage 300 using Basic authentication.

     Connect Using Basic Authentication

     You must provide values for the following properties to successfully authenticate to Sage 300. Note that the provider reuses the session opened by Sage 300 using cookies. This means that your credentials are used only on the first request to open the session. After that, cookies returned from Sage 300 are used for authentication.

     • Url: Set this to the url of the server hosting Sage 300. Construct a URL for the Sage 300 Web API as follows: {protocol}://{host-application-path}/v{version}/{tenant}/ For example, http://localhost/Sage300WebApi/v1.0/-/.
     • User: Set this to the username of your account.
     • Password: Set this to the password of your account.

     Built-in Connection String Designer

     For assistance in constructing the JDBC URL, use the connection string designer built into the Sage 300 JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

     java -jar cdata.jdbc.sage300.jar

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

   • Set Import Query to a SQL query that will extract the data you want from Sage 300, i.e.:
     SELECT * FROM OEInvoices
4. From the "Sink" tab, click to add a destination sink (we use Google BigQuery in this example)
5. Click "Properties" on the BigQuery sink to edit the properties
   • Set the Label
   • Set Reference Name to a value like sage300-bigquery
   • Set Project ID to a specific Google BigQuery Project ID (or leave as the default, "auto-detect")
   • Set Dataset to a specific Google BigQuery dataset
   • Set Table to the name of the table you wish to insert Sage 300 data into

With the Source and Sink configured, you are ready to pipe Sage 300 data into Google BigQuery. Save and deploy the pipeline. When you run the pipeline, Google Data Fusion will request live data from Sage 300 and import it into Google BigQuery.

While this is a simple pipeline, you can create more complex Sage 300 pipelines with transforms, analytics, conditions, and more. Download a free, 30-day trial of the CData JDBC Driver for Sage 300 and start working with your live Sage 300 data in Google Data Fusion today.