VOOZH about

URL: https://www.cdata.com/kb/tech/azuredatalake-cloud-redash.rst

⇱ Query, Visualize, and Share live Azure Data Lake Storage Data in Redash

Query, Visualize, and Share live Azure Data Lake Storage Data in Redash

👁 Mohsin Turki
Mohsin Turki
Technical Marketing Engineer
Use CData Connect AI to connect to live Azure Data Lake Storage data in Redash for querying, visualizing, and sharing.

Redash is a collaboration tool that lets you query, visualize, and share your data. When paired with CData Connect AI, Redash gets access to live Azure Data Lake Storage data. This article demonstrates how to connect to Azure Data Lake Storage using Connect AI and work with live Azure Data Lake Storage data in Redash.

CData Connect AI provides a pure SQL Server interface for Azure Data Lake Storage, allowing you to query data from Azure Data Lake Storage without replicating the data to a natively supported database. Using optimized data processing out of the box, CData Connect AI pushes all supported SQL operations (filters, JOINs, etc.) directly to Azure Data Lake Storage, leveraging server-side processing to return the requested Azure Data Lake Storage data quickly.

Configure Azure Data Lake Storage Connectivity for Redash

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

  1. Log into Connect AI, click Sources, and then click Add Connection
    2. 👁 Adding a Connection
  2. Select "Azure Data Lake Storage" from the Add Connection panel
    3. 👁 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 Redash.

Connect to Azure Data Lake Storage from Redash using Connect AI

To establish a connection from Redash to CData Connect AI using the Virtual SQL Server, follow these steps:

  1. Log into Redash.
  2. Click the settings widget on the top right.
  3. Click New Data Source.
  4. Select Microsoft SQL Server as the Data Source Type.
  5. On the configuration tab, set the following properties:
    • Database Name: enter the Connection Name of the CData Connect AI data source you want to connect to (for example, Salesforce1).
    • Server: enter the virtual SQL Server host name (tds.cdata.com)
    • User: enter your CData Connect AI username. This is displayed in the top-right corner of the CData Connect AI interface. For example, [email protected].
    • Password: enter the PAT you generated on the Settings page.
    • Port: enter 14333
    👁 Configuring the connection to Connect AI from Redash.
  6. Click Create.
  7. Click Test Connection to ensure that the connection is configured properly.

You can now work with live Azure Data Lake Storage data in Redash.

Get CData Connect AI

To get live data access to hundreds of SaaS, Big Data, and NoSQL sources directly from Redash, try CData Connect AI today!