Ready to get started?

Learn more about the CData JDBC Driver for Bugzilla or download a free trial:

Download Now

Pipe Bugzilla Data in Google Data Fusion

Load the CData JDBC Driver into Google Data Fusion and pipe live Bugzilla data to any supported data platform.

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

Upload the CData JDBC Driver for Bugzilla to Google Data Fusion

Upload the CData JDBC Driver for Bugzilla to your Google Data Fusion instance to work with live Bugzilla 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 -.jar. For example: cdata.jdbc.bugzilla-2019.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.bugzilla) and make note of the name
    • Class name: Set the JDBC class name: (cdata.jdbc.bugzilla.BugzillaDriver)
  5. Click "Finish"

Pipe Bugzilla Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live Bugzilla 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
    • Set the Label
    • Set Reference Name to a value for any future references (i.e.: cdata-bugzilla)
    • Set Plugin Type to "jdbc"
    • Set Connection String to the JDBC URL for Bugzilla. For example:

      jdbc:bugzilla:5246...;Url=http://yourdomain/Bugzilla;APIKey=abc123;

      You can authenticate to your Bugzilla account using two parameters:

      • URL: The URL of your Bugzilla developer's page (the Home page).
      • ApiKey: API Keys can be generated from the Preferences -> API Keys section of your Bugzilla developer's page.

      To use the JDBC Driver in Google Data Fusion, you will need to set the RTK property in the JDBC URL. You can view the licensing file included in the installation for information on how to set this property.

      Built-in Connection String Designer

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

      java -jar cdata.jdbc.bugzilla.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 Bugzilla, i.e.:
      SELECT * FROM Bugs
  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 bugzilla-bigquery
    • Set Projcect 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 Bugzilla data into

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

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