Build Azure Table-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 Azure enables users to access live Azure Table data from within their Google Data Fusion pipelines. While the CData JDBC Driver enables piping Azure Table data to any data source natively supported in Google Data Fusion, this article walks through piping data from Azure Table to Google BigQuery,

Upload the CData JDBC Driver for Azure to Google Data Fusion

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

Connect to Azure Table Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live Azure Table 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-azuretables)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for Azure Table. For example:
     
     jdbc:azuretables:RTK=5246...;AccessKey=myAccessKey;Account=myAccountName;

     Specify your AccessKey and your Account to connect. Set the Account property to the Storage Account Name and set AccessKey to one of the Access Keys. Either the Primary or Secondary Access Keys can be used. To obtain these values, navigate to the Storage Accounts blade in the Azure portal. You can obtain the access key by selecting your account and clicking Access Keys in the Settings section.

     Built-in Connection String Designer

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

     java -jar cdata.jdbc.azuretables.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 Azure Table, i.e.:
     SELECT * FROM NorthwindProducts
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 azuretables-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 Azure Table data into

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

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