How to integrate Metabase with Azure Data Lake Storage Data

Metabase is an open source data visualization tool that allows users to create interactive dashboards. When paired with CData Connect AI, users can easily create visualizations and dashboards linked to live Azure Data Lake Storage data. This article describes how to connect to Azure Data Lake Storage and build a simple visualization using Azure Data Lake Storage data.

CData Connect provides a pure cloud-to-cloud interface for Azure Data Lake Storage, allowing you to easily integrate with live Azure Data Lake Storage data in Metabase — without replicating the data. Connect looks exactly like a SQL Server database to Metabase and uses optimized data processing out of the box to push 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 Metabase

Connectivity to Azure Data Lake Storage from Metabase is made possible through CData Connect AI. To work with Azure Data Lake Storage data from Metabase, 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 Metabase.

Connect to CData Connect AI from Metabase

After creating the connection in Connect AI, navigate to your Metabase instance. Use the SQL Server interface to connect to Connect AI.

1. Navigate to the administration screen (Settings -> Admin) and click "Add Database" from the "Databases" tab 👁 Adding a new database connection to Metabase.
   
2. Configure the connection to Connect AI and click "Save"
   • Database type: Select "SQL Server"
   • Name: Name the connection (e.g. "Azure Data Lake Storage (Connect AI)")
   • Host: tds.cdata.com
   • Port: 14333
   • Database name: The name of the connection you just created (e.g. ADLS1)
   • Username: A Connect AI username (e.g. [email protected])
   • Password: The PAT previously created
   • Click to Use a secure connection (SSL)
   👁 Configuring the connection to Connect AI.
   

Execute Azure Data Lake Storage Data with Metabase

Once you configure the connection to Connect AI, you can query Azure Data Lake Storage and build visualizations.

1. Use the "Write SQL" tool to retrieve the Azure Data Lake Storage data 👁 Click the 'Write SQL' button.
   
2. Write a SQL query based on the Azure Data Lake Storage connection in CData Connect AI, e.g.
   
   

   SELECT FullPath, Permission FROM Resources WHERE Type = 'FILE'

   👁 Collected data (Salesforce is shown).
   
3. Navigate to the "Visualization" screen, choose a visualization, and configure the visualization 👁 Collected data (Salesforce is shown).
   

More Information & Free Trial

At this point, you have built a simple visualization from Azure Data Lake Storage data in Metabase. You can continue to work with live Azure Data Lake Storage data in Metabase just like you would any SQL Server database. For more information on creating a live connection to Azure Data Lake Storage (and more than 100 other data sources), visit the Connect AI page. Sign up for a free trial and start working with live Azure Data Lake Storage data in Metabase today.