Build Kafka-Connected ETL Processes in Google Data Fusion

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Apache Kafka JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Apache Kafka.



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

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

Upload the CData JDBC Driver for Kafka to Google Data Fusion

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

Connect to Kafka Data in Google Data Fusion

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

      jdbc:apachekafka:RTK=5246...;User=admin;Password=pass;BootStrapServers=https://localhost:9091;Topic=MyTopic;

      Set BootstrapServers and the Topic properties to specify the address of your Apache Kafka server, as well as the topic you would like to interact with.

      Authorization Mechanisms

      • SASL Plain: The User and Password properties should be specified. AuthScheme should be set to 'Plain'.
      • SASL SSL: The User and Password properties should be specified. AuthScheme should be set to 'Scram'. UseSSL should be set to true.
      • SSL: The SSLCert and SSLCertPassword properties should be specified. UseSSL should be set to true.
      • Kerberos: The User and Password properties should be specified. AuthScheme should be set to 'Kerberos'.

      You may be required to trust the server certificate. In such cases, specify the TrustStorePath and the TrustStorePassword if necessary.

      Built-in Connection String Designer

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

      java -jar cdata.jdbc.apachekafka.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 Kafka, i.e.:
      SELECT * FROM SampleTable_1
  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 apachekafka-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 Kafka data into

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

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