Analyze Azure Data Lake Storage Data in Looker

Looker is a business intelligence and big data analytics platform that helps you explore, analyze and share real-time business analytics. When paired with CData Connect AI, you get instant, cloud-to-cloud access to Azure Data Lake Storage data for business applications. This article shows how to connect to Azure Data Lake Storage in Connect AI and then connect to Azure Data Lake Storage data in Looker.

CData Connect AI provides a pure cloud-to-cloud interface for Azure Data Lake Storage, allowing you to build reports from live Azure Data Lake Storage data in Looker — without replicating the data to a natively supported database. As you create applications to work with data, Looker generates SQL queries to gather data. 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 quickly return the requested Azure Data Lake Storage data.

Configure Azure Data Lake Storage Connectivity for Looker

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

Connect to Azure Data Lake Storage in Looker

The steps below outline connecting to CData Connect AI from Looker to create a new Azure Data Lake Storage data source.

1. Log-in to Looker
2. In the navigation pane, select Admin. 👁 Selecting Admin
   
3. Under the Database category, select Connections. 👁 Selecting connections
   
4. On the Connections page, click Add Connection. 👁 Adding a new connection
   
5. Enter the connection settings:
   • Name: the name for the connection in models.
   • Dialect: select Microsoft SQL Server 2017+.
   • SSH Server: leave this disabled.
   • Remote Host:Port: enter tds.cdata.com in the first field and 14333 in the second field.
   • Database: enter the Connection Name of the CData Connect AI data source you want to connect to (for example, QuickBooksOnline1).
   • 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 generated on the Settings page
   Leave the rest of the connection settings at their default values unless you need to modify them. 👁 Configuring connection settings
   
6. At the bottom of the page, click Test These Settings to ensure that you can connect to CData Connect AI.
7. Click Add Connection to create the connection and return to the Connections page. 👁 New connection added.
   

Your connection is now available for use in Looker. To connect to additional data sources from your CData Connect AI account, repeat the setup steps above, changing the value for Database for each data source.

Creating A Looker Visualization From The SQL Runner and Explore Features

To create a visualization in Looker using the SQL Runner, follow these steps:

1. In the Looker interface, select Develop -> SQL Runner from the left navigation pane. 👁 Select SQL Runner.
   
2. On the SQL Runner interface, select the connection you made in the previous steps. 👁 Entering name for new dashboard.
   
3. Now, click the gear symbol next to a table and then select Explore Table. 👁 Entering name for new dashboard.
   
4. Next, on the left menu, select fields from the table and click Run. You can now expand the Visualization accordion, and see a bar chart, by default. 👁 Entering name for new dashboard.
   

   We have now created a visualization of Azure Data Lake Storage data in Looker using CData Connect AI!

Get CData Connect AI

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