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

Upload the CData JDBC Driver for Apache Spark to Google Data Fusion

Upload the CData JDBC Driver for Apache Spark to your Google Data Fusion instance to work with live Spark 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: cdatasparksql-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.sparksql) and make note of the name
   • Class name: Set the JDBC class name: (cdata.jdbc.sparksql.SparkSQLDriver)
   👁 Configuring the driver (Salesforce is shown.)
   
5. Click "Finish"

Connect to Spark Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live Spark 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-sparksql)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for Spark. For example:
     
     jdbc:sparksql:RTK=5246...;Server=127.0.0.1;

     Set the Server, Database, User, and Password connection properties to connect to SparkSQL.

     Built-in Connection String Designer

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

      java -jar cdata.jdbc.sparksql.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 Spark, i.e.:
     SELECT * FROM Customers
   👁 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 sparksql-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 Spark data into
   👁 Configuring the BigQuery sink
   

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

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