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Pipe Presto Data in Google Data Fusion

Load the CData JDBC Driver into Google Data Fusion and pipe live Presto 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 Presto enables users to access live Presto data from within their Google Data Fusion pipelines. While the CData JDBC Driver enables piping Presto data to any data source natively supported in Google Data Fusion, this article walks through piping data from Presto to Google BigQuery,

Upload the CData JDBC Driver for Presto to Google Data Fusion

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

Pipe Presto Data in Google Data Fusion

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

      jdbc:presto:5246...;Server=127.0.0.1;Port=8080;

      Set the Server and Port connection properties to connect, in addition to any authentication properties that may be required.

      To enable TLS/SSL, set UseSSL to true.

      Authenticating with LDAP

      In order to authenticate with LDAP, set the following connection properties:

      • AuthScheme: Set this to LDAP.
      • User: The username being authenticated with in LDAP.
      • Password: The password associated with the User you are authenticating against LDAP with.

      Authenticating with Kerberos

      In order to authenticate with KERBEROS, set the following connection properties:

      • AuthScheme: Set this to KERBEROS.
      • KerberosKDC: The Kerberos Key Distribution Center (KDC) service used to authenticate the user.
      • KerberosRealm: The Kerberos Realm used to authenticate the user with.
      • KerberosSPN: The Service Principal Name for the Kerberos Domain Controller.
      • KerberosKeytabFile: The Keytab file containing your pairs of Kerberos principals and encrypted keys.
      • User: The user who is authenticating to Kerberos.
      • Password: The password used to authenticate to Kerberos.

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

      java -jar cdata.jdbc.presto.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 Presto, i.e.:
      SELECT * FROM Customer
  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 presto-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 Presto data into

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

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