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

Upload the CData JDBC Driver for Airtable to Google Data Fusion

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

Connect to Airtable Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live Airtable 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-airtable)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for Airtable. For example:
     
     jdbc:airtable:RTK=5246...;APIKey=keymz3adb53RqsU;BaseId=appxxN2fe34r3rjdG7;TableNames=Table1,...;ViewNames=Table1.View1,...;

     APIKey, BaseId and TableNames parameters are required to connect to Airtable. ViewNames is an optional parameter where views of the tables may be specified.

     • APIKey : API Key of your account. To obtain this value, after logging in go to Account. In API section click Generate API key.
     • BaseId : Id of your base. To obtain this value, it is in the same section as the APIKey. Click on Airtable API, or navigate to https://airtable.com/api and select a base. In the introduction section you can find "The ID of this base is appxxN2ftedc0nEG7."
     • TableNames : A comma separated list of table names for the selected base. These are the same names of tables as found in the UI.
     • ViewNames : A comma separated list of views in the format of (table.view) names. These are the same names of the views as found in the UI.

     Built-in Connection String Designer

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

      java -jar cdata.jdbc.airtable.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 Airtable, i.e.:
     SELECT * FROM SampleTable_1
   👁 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 airtable-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 Airtable data into
   👁 Configuring the BigQuery sink
   

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

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