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

Upload the CData JDBC Driver for Azure Data Lake Storage to Google Data Fusion

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

Connect to Azure Data Lake Storage Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live Azure Data Lake Storage 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-adls)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for Azure Data Lake Storage. For example:
     
     jdbc:adls:RTK=5246...;Schema=ADLSGen2;Account=myAccount;FileSystem=myFileSystem;AccessKey=myAccessKey;InitiateOAuth=GETANDREFRESH;

     Authenticating to a Gen 1 DataLakeStore Account

     Gen 1 uses OAuth 2.0 in Azure AD for authentication.

     For this, an Active Directory web application is required. You can create one as follows:

     1. Sign in to your Azure Account through the .
     2. Select "Azure Active Directory".
     3. Select "App registrations".
     4. Select "New application registration".
     5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
     6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
     7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

     To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

     • Schema: Set this to ADLSGen1.
     • Account: Set this to the name of the account.
     • OAuthClientId: Set this to the application Id of the app you created.
     • OAuthClientSecret: Set this to the key generated for the app you created.
     • TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
     • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

     Authenticating to a Gen 2 DataLakeStore Account

     To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

     • Schema: Set this to ADLSGen2.
     • Account: Set this to the name of the account.
     • FileSystem: Set this to the file system which will be used for this account.
     • AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
     • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

     Built-in Connection String Designer

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

     java -jar cdata.jdbc.adls.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 Data Lake Storage, i.e.:
     SELECT * FROM Resources
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 adls-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 Data Lake Storage data into

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

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