Create Reports from Azure Data Lake Storage Data in Looker Studio

Looker Studio, formerly known as Google Data Studio, empowers users to craft customized reports featuring data visualizations that can be shared with clients while reflecting your brand identity. When combined with CData Connect AI, you gain immediate cloud-to-cloud access to Azure Data Lake Storage data to create visualizations, dashboards, and more. This article provides step-by-step instructions on establishing a virtual database for Azure Data Lake Storage and generating reports from Azure Data Lake Storage data within Looker Studio.

CData Connect AI offers a seamless cloud-to-cloud interface tailored for Azure Data Lake Storage, making it straightforward to construct reports directly from live Azure Data Lake Storage data within Looker Studio without the need for data replication. As you create visualizations, Looker Studio generates queries to retrieve data. With its inherent optimized data processing capabilities, CData Connect AI efficiently channels all supported query operations, including filters, JOINs, and more, directly to Azure Data Lake Storage. This leverages server-side processing to swiftly provide the requested Azure Data Lake Storage data.

Configure Azure Data Lake Storage Connectivity for Looker Studio

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

With the connection configured, you are ready to connect to Azure Data Lake Storage data from Looker Studio.

Visualize Live Azure Data Lake Storage Data from Looker Studio

The steps below outline connecting to CData Connect AI from Looker Studio to create a new Azure Data Lake Storage data source and build a simple visualization from the data.

1. Log into Looker Studio, click data sources, create a new data source, and choose CData Connect AI Connector.
👁 Create a new connection in Looker Studio

2. Click Authorize and allow access to your Google account. 👁 Granting permissions to the Connector
   
3. Click Authorize to authenticate with your CData Connect AI instance 👁 Authenticating with CData Connect AI
   
4. In the CData Connect AI Connector in Looker Studio select Connections to import from the dropdown and click Next
👁 Importing from Connections

5. Now select a Connection (e.g. ADLS1) and click Next 👁 Selecting a Connection
   
6. Select a Table (e.g. Resources) or use a Custom Query and click Connect to continue 👁 Selecting a Table
   
7. If needed, modify columns, click Create Report, and add the data source to the report.
👁 Configuring column definitions

8. Select a visualization style and add it to the report.
9. Select Dimensions and Measures to customize your visualization.
👁 Visualizing Azure Data Lake Storage data in Looker Studio

Live Access to Azure Data Lake Storage Data from Cloud Applications

Now you have a direct, cloud-to-cloud connection to live Azure Data Lake Storage data from your Looker Studio workbook. You can create more data sources and new visualizations, build reports, and more — all without replicating Azure Data Lake Storage data.

Try CData Connect AI and get real-time data access to hundreds of SaaS, Big Data, and NoSQL sources directly from your cloud applications.