Query Azure Data Lake Storage Data as a SQL Server Database in Node.js

You can use CData Connect AI to query Azure Data Lake Storage data through a SQL Server interface. Follow the procedure below to create a virtual database for Azure Data Lake Storage in Connect AI and start querying using Node.js.

CData Connect AI provides a pure MySQL, cloud-to-cloud interface for Azure Data Lake Storage, allowing you to easily query live Azure Data Lake Storage data in Node.js — without replicating the data to a natively supported database. As you query data in Node.js, CData Connect AI pushes all supported SQL operations (filters, JOINs, etc) directly to Azure Data Lake Storage, leveraging server-side processing to quickly return Azure Data Lake Storage data.

Configure Azure Data Lake Storage Connectivity for NodeJS

Connectivity to Azure Data Lake Storage from NodeJS is made possible through CData Connect AI. To work with Azure Data Lake Storage data from NodeJS, we start by creating and configuring a Azure Data Lake Storage connection.

1. Log into Connect AI, click Sources, and then click Add Connection
👁 Adding a Connection

2. Select "Azure Data Lake Storage" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Azure Data Lake Storage.

   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.
   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Azure Data Lake Storage Connection page and update the User-based permissions. 👁 Updating permissions
   

Add a Personal Access Token

When connecting to Connect AI through the REST API, the OData API, or the Virtual SQL Server, a Personal Access Token (PAT) is used to authenticate the connection to Connect AI. It is best practice to create a separate PAT for each service to maintain granularity of access.

1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create. 👁 Creating a new PAT
   
4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, you are ready to connect to Azure Data Lake Storage data from Node.js.

Query Azure Data Lake Storage from Node.js

The following example shows how to define a connection and execute queries to Azure Data Lake Storage with the SQL Server module. You will need the following information:

• server: tds.cdata.com
• port: 14333
• user: a Connect AI user (e.g. [email protected])
• password: the PAT for the above user
• database: The connection you configured for Azure Data Lake Storage (ADLS1)

Connect to Azure Data Lake Storage data and start executing queries with the code below:

var sql = require('mssql')
var config = {
	server: 'tds.cdata.com',
	port: 14333, 
	user: '[email protected]', //update me
	password: 'CONNECT_USER_PAT', //update me	
	options: {
		encrypt: true,
		database: 'ADLS1'
	}
}

sql.connect(config, err => { 
 if(err){
 throw err ;
 }
 new sql.Request().query('SELECT * FROM Resources', (err, result) => {
 console.dir(result)
 })
 
});

sql.on('error', err => {
 console.log("SQL Error: " ,err);
})