Create Azure Data Lake Storage-Connected Dashboards in Grafana

Grafana is an open-source platform that allows users to analyze, monitor, and visualize telemetry from various data sources. When combined with CData Connect AI, you gain immediate access to Azure Data Lake Storage data for business dashboards. This article outlines the process of connecting to Azure Data Lake Storage using Connect AI and creating a basic dashboard from Azure Data Lake Storage data within Grafana.

CData Connect AI offers a pure SQL Server, cloud-to-cloud interface for Azure Data Lake Storage, enabling the creation of dashboards directly from live Azure Data Lake Storage data within Grafana, all without the need for data replication to a native database. As you build visualizations, Grafana generates SQL queries to gather data. With its inherent optimized data processing capabilities, CData Connect AI efficiently channels all supported SQL operations, including filters and JOINs, directly to Azure Data Lake Storage. This leverages server-side processing to swiftly deliver the requested Azure Data Lake Storage data.

Configure Azure Data Lake Storage Connectivity for Grafana

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

Visualize Live Azure Data Lake Storage Data in Grafana

To establish a connection from Grafana to the CData Connect AI Virtual SQL Server API, follow these steps.

1. If you have not already done so, download and install Grafana for your operating system from the Grafana Website. Once installed, access Grafana at http://localhost:3000/.
2. Log in to Grafana with your username and password for Grafana. If this is your first time logging in, the username is admin and the password is admin.
3. On the navigation menu, click Sources > Add new connection. On this page, you can search for Microsoft SQL Server and select it as your data source. 👁 Adding a new Connection in Grafana
   
4. Select Microsoft SQL Server and click Add new data source. 👁 Adding a new Connection in Grafana
   
5. Enter a name for the new data source and then enter the following connection settings:
   • Host: tds.cdata.com:14333
   • Database: enter the Connection Name of the CData Connect AI data source you want to connect to (for example, ADLS1).
   • Username: 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 previously generated.
   👁 Configure connection to the Connect AI Virtual SQL Server API. (Salesforce shown)
   
6. Click Save & Test. A Database Connect OK message appears when you have a successful connection established.

Create a Dashboard

Once you create the data source for Azure Data Lake Storage, you can start building dashboards on Azure Data Lake Storage data. Start in the navigation menu and click Dashboards

1. On the Dashboards page, click + Create dashboard, then click + Add visualization.
2. This opens the Select data source window where you can select your created connection. 👁 Select your connection. (Salesforce shown)
   
3. After selecting your connection, you can choose the tables and columns that you want to query for your visualization. Then press Run Query to run the generated query. 👁 Select the tables and columns you want to query. (Salesforce shown)
   
4. After running your query, the resulting data is populated above the query editor. Here you can select the visualization that you want to use to present your data on the dashboard panel. 👁 Visualize your queried data. (Salesforce shown)
   
5. When you have finished editing your panel, you can click Save dashboard to save the changes made to your Dashboard.

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