Build Lakebase-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 Lakebase enables users to access live Lakebase data from within their Google Data Fusion pipelines. While the CData JDBC Driver enables piping Lakebase data to any data source natively supported in Google Data Fusion, this article explains how to pipe data from Lakebase to Google BigQuery,

Upload the CData JDBC Driver for Lakebase to Google Data Fusion

Upload the CData JDBC Driver for Lakebase to your Google Data Fusion instance to work with live Lakebase 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: cdatalakebase-2020.jar

1. Open your Google Data Fusion instance
2. Click the to add an entity and upload a driver 👁 Image
   
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.lakebase) and make note of the name
   • Class name: Set the JDBC class name: (cdata.jdbc.lakebase.LakebaseDriver)
   👁 Configuring the driver (Salesforce is shown.)
   
5. Click "Finish"

Connect to Lakebase Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live Lakebase 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 👁 Adding a database source
   
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-lakebase)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for Lakebase. For example:
     
     jdbc:lakebase:RTK=5246...;DatabricksInstance=lakebase;Server=127.0.0.1;Port=5432;Database=my_database;InitiateOAuth=GETANDREFRESH; To connect to Databricks Lakebase, start by setting the following properties:
     • DatabricksInstance: The Databricks instance or server hostname, provided in the format instance-abcdef12-3456-7890-abcd-abcdef123456.database.cloud.databricks.com.
     • Server: The host name or IP address of the server hosting the Lakebase database.
     • Port (optional): The port of the server hosting the Lakebase database, set to 5432 by default.
     • Database (optional): The database to connect to after authenticating to the Lakebase Server, set to the authenticating user's default database by default.

     OAuth Client Authentication

     To authenicate using OAuth client credentials, you need to configure an OAuth client in your service principal. In short, you need to do the following:

     1. Create and configure a new service principal
     2. Assign permissions to the service principal
     3. Create an OAuth secret for the service principal

     For more information, refer to the Setting Up OAuthClient Authentication section in the Help documentation.

     OAuth PKCE Authentication

     To authenticate using the OAuth code type with PKCE (Proof Key for Code Exchange), set the following properties:

     • AuthScheme: OAuthPKCE.
     • User: The authenticating user's user ID.

     For more information, refer to the Help documentation.

     Built-in Connection String Designer

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

      java -jar cdata.jdbc.lakebase.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.)
     
   • Set Import Query to a SQL query that will extract the data you want from Lakebase, i.e.:
     SELECT * FROM Orders
   👁 Configuring the database source
   
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 lakebase-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 Lakebase data into
   👁 Configuring the BigQuery sink
   

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

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