How to Work with Azure Data Lake Storage Data in ETL Validator JDBC

ETL Validator provides data movement and transformation capabilities for integrating data platforms across your organization. CData's JDBC driver seamlessly integrates with ETL Validator and extends its native connectivity to include Azure Data Lake Storage data.

This tutorial explains how to build a simple ETL validator data flow to extract data from Azure Data Lake Storage data and load it into an example data storage solution: SQL Server.

Add a new ETL Validator data source via CData

CData extends ETL Validator's data connectivity capabilities by providing the ability to add data sources that connect via CData's JDBC drivers. Connecting to Azure Data Lake Storage data simply requires creating a new data source in ETL Validator through CData's connectiviy suite as described below.

Login to ETL Validator

Begin by logging into ETL Validator to view the application dashboard.

👁 Access the ETL Validator dashboard

Click on Add a DataSource

CData extends the data source options within ETL Validator.

👁 Create a new DataSource

Click on CData

CData's connectivity is embedded within ETL Validator's data source options.

👁 CData data source

Configure the CData Driver Connection String

You will need a JDBC connection string to establish a connection to Azure Data Lake Storage in ETL Validator.

👁 CData data source

Authenticating to a Gen 1 DataLakeStore Account

Gen 1 uses OAuth 2.0 in Entra ID (formerly 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 "Entra ID" (formerly Azure AD).
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

👁 Using the built-in connection string designer to generate a JDBC URL (azure data lake storage is shown.)

A typical connection string looks like this:

jdbc:adls:Schema=ADLSGen2;Account=myAccount;FileSystem=myFileSystem;AccessKey=myAccessKey;InitiateOAuth=GETANDREFRESH;

Licensing the Driver

To ensure the JDBC driver is licensed appropriately, copy the license file to the appropriate location:

Copy the JDBC Driver for Azure Data Lake Storage and lic file from "C:\Program Files\CData[product_name]\lib" to "C:\Datagaps\ETLValidator\Server\apache-tomcat\bin".

 cdata.jdbc.adls.jar
 cdata.jdbc.adls.lic
 

Note: If you do not copy the .lic file with the jar, you will see a licensing error that indicates you do not have a valid license installed. This is true for both the trial and full versions.

Save the connection

Should you encounter any difficulties loading the CData JDBC driver class, please contact DataGap's team, and they will provide you instructions on how to load the jar file for the relevant driver.

Add SQL Server as a Target

This example will use SQL Server as a destination for Azure Data Lake Storage data data, but any preferred destination can be used instead.

Go to DataSources and select MS_SQL_SERVER

This option is the default.

👁 Add SQL Server

Fill in the necessary connection details and test the connection

The details will depend on the specific target, but these details may include a URL, authentiation credentials, etc.

👁 Add SQL Server

Create a Dataflow in ETL Validator

Open the Dataflows tab

Configured data flows will appear in this window.

👁 Dataflows tab

Select Create Dataflow

Name your new dataflow and save it.

Open the Dataflow to view the Dataflow Diagram

The details of the data movement will be configured in this panel.

👁 Dataflow diagram

Drag & drop the JDBC as a source from the right side

Give the new source an appropriate name and save it.

👁 Jira example source

Fill in the Query section of the new source

Select the Table from the Schema option that reflects which data should be pulled from Azure Data Lake Storage data.

View the expected results of your query

The anticipated outcome of the configured query is displayed in the Result tab.

👁 Query results

Add the destination to the Dataflow

Select Switch to Diagram, then drag & drop the DB Sink as a target from the right side (under Sink options). Give the sink an appropriate name and save it.

👁 Data sink

Set the appropriate Schema for the destination

Choose the Schema and table that matches the structure of the source table. For this example, the table on the target side was created to match the Source so that data flow seamlessly. More advanced schema transformation operations are beyond the scope of this article.

👁 Destination schema

Hit the RUN option to begin replication

Running the job will take some time.

👁 Destination schema

View the finished Dataflow

Return to the diagram to see the finished data replication job from Azure Data Lake Storage data to SQL Server.

👁 Full dataflow

Get Started Today

Download a free, 30-day trial of the CData JDBC Driver for Azure Data Lake Storage and start building Azure Data Lake Storage-connected applications with ETL Validator. Reach out to our Support Team if you have any questions.