Ready to get started?

Learn more about Connectivity Solutions

Learn More

Build -Connected ETL Processes in Google Data Fusion

Load the CData JDBC Driver into Google Data Fusion and create ETL processes with access live data.

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

Upload the CData JDBC Driver for to Google Data Fusion

Upload the CData JDBC Driver for to your Google Data Fusion instance to work with live 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.avalaraavatax-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.avalaraavatax) and make note of the name
    • Class name: Set the JDBC class name: (cdata.jdbc.avalaraavatax.AvalaraAvataxDriver)
  5. Click "Finish"

Connect to Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live 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-avalaraavatax)
    • Set Plugin Type to "jdbc"
    • Set Connection String to the JDBC URL for . For example:

      jdbc:avalaraavatax:RTK=5246...;User=MyUser;Password=MyPassword;

      The primary method for performing basic authentication is to provide your login credentials, as follows:

      • User: Set this to your username.
      • Password: Set this to your password.

      Optionally, if you are making use of a sandbox environment, set the following:

      • UseSandbox: Set this to true if you are authenticating with a sandbox account.

      Authenticating Using Account Number and License Key

      Alternatively, you can authenticate using your account number and license key. Connect to data using the following:

      • AccountId: Set this to your Account Id. The Account Id is listed in the upper right hand corner of the admin console.
      • LicenseKey: Set this to your Avalara Avatax license key. You can generate a license key by logging into Avalara Avatax as an account adminstrator and navigating to Settings -> Reset License Key.

      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 JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

      java -jar cdata.jdbc.avalaraavatax.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 , i.e.:
      SELECT * FROM Transactions
  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 avalaraavatax-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 data into

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

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