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

⇱ Build Apps with Live Azure Data Lake Storage data in Adalo using CData Connect AI

Build Apps with Live Azure Data Lake Storage data in Adalo using CData Connect AI

👁 Mohsin Turki
Mohsin Turki
Technical Marketing Engineer
Use CData Connect AI to integrate live Azure Data Lake Storage data into the Adalo platform and build custom mobile and web applications with real-time Azure Data Lake Storage data.

Adalo is a no-code platform used to build custom mobile and web applications without needing programming skills. It allows users to design interfaces, manage databases, and integrate functionality through simple drag-and-drop tools, making app development accessible and efficient. When combined with CData Connect AI, it provides access to Azure Data Lake Storage data to build custom applications and more.

This article explains how to use CData Connect AI to create a live connection to Azure Data Lake Storage and how to connect and access live Azure Data Lake Storage data from the Adalo platform.

Configure Azure Data Lake Storage connectivity for Adalo in CData Connect AI

To work with Azure Data Lake Storage data in Adalo, we need to connect to Azure Data Lake Storage from Connect AI, provide user access to the connection, and create OData endpoints for the Azure Data Lake Storage data.

Connect to Azure Data Lake Storage from Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

  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, Workspaces, 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.

Configure Azure Data Lake Storage Endpoints for Adalo

After connecting to Azure Data Lake Storage, create a workspace for your desired table(s).

  1. Navigate to the Workspaces page and click Add to create a new Workspace (or select an existing workspace). 👁 The Workspaces page.
    👁 Adding a new Workspace.
  2. Click Add to add new assets to the Workspace.
  3. Select the Azure Data Lake Storage connection (e.g. ADLS1) and click Next. 👁 Selecting an Asset (Salesforce is shown).
  4. Select the table(s) you wish to work with and click Confirm. 👁 Selecting Tables (Salesforce is shown).
  5. Make note of the OData Service URL for your workspace, e.g. https://cloud.cdata.com/api/odata/{workspace_name}

With the connection, PAT, and Workspace configured, you are ready to connect to Azure Data Lake Storage data from Adalo.

Build a custom app with Adalo

After configuring CData Connect AI, you can begin developing the Adalo app using Azure Data Lake Storage data.

  1. Click on CREATE NEW APP. 👁 Create a new app.
  2. Select Desktop Web App and click on Next. 👁 Select the kind of app to be created.
  3. You can choose any template, but for testing purposes, we've chosen a Blank template. 👁 Select the preferred template.
  4. Finally, add an app name and choose a color scheme, and your app is ready to go! 👁 Add a name and color scheme to the app.

Registering external collections

Adalo uses a data structure called Collection to manage app data and offers a feature called External Collections with APIs, which lets you seamlessly integrate Collection with external APIs. Use this feature to connect to Connect AI.

  1. After creating an app, you first need to register a Collection, which is a definition for handling data within the Adalo app. There are two types of Collections: Database Collections, which are like Adalo's own internal database, and External Collections, which allow access to external APIs. In this case, we will access an external API, registering it as an External Collection. 👁 Add a new collection under Database Collections.
  2. On the External Collections registration screen, click on ADD COLLECTION to enter the collection name (any name of your choice), and provide the API base URL endpoint (OData endpoint registered on Connect AI). Make sure to add "/(table)" to the OData URL to access the specific endpoint. (Refer to the image given for reference) 👁 Add a name and provide an API URL endpoint or OData endpoint to the collection.
  3. In order to authenticate, you need a base64 authorization header comprising your username and PAT, joined by a colon. For example, [email protected]:MY_PAT. You can use a scripting tool, web tool, or other application to create this header.

    Using Postman to generate the Authorization header

    1. Open the Postman application. Enter the OData URL mentioned in the previous step for a GET request on Postman. Click on the Authorization tab and select Auth Type as "Basic Auth". Enter the Username and Password as follows and click on Send:
      • Username: Connect AI Username
      • Password: PAT generated on Connect AI
      👁 Add username and password to authorize the OData endpoint in Postman.
    2. Go to the Headers tab and copy/save the value of the Authorization key. 👁 Obtain the Authourization key from Postman.
  4. Back in Adalo, click on ADD ITEM and select Header. 👁 Open to add the collection header.
  5. Enter the details in the Header section as follows:
    • Name: "Authorization"
    • Value: Authorization key value copied in the previous step
    👁 Configure the collection header with the Authorization key.
  6. Select Done and click on Next.
  7. Select Get All and enter "value" in the Results Key section. Click on Done and Next. 👁 Enter the results key.
  8. Now, to test the API setup, click on RUN TEST. If you finally see the message and response shown below, the creation of External Collections is complete. 👁 Run the test.

Creating a list screen

After configuring CData Connect AI, you can now begin developing the app on Adalo using Azure Data Lake Storage data.

  1. First, click the + button in the upper right corner of the Adalo screen to display the list screen, then drag and drop "Simple List" onto the screen. 👁 Add a list to the app.
  2. Once the layout is complete as shown below, link the External Collections definition you created earlier to What is this a list of? 👁 Mention the list type
  3. Click on "Add Magic Text" button (as shown) to add the "Title" and "Subtitle" to the data items you want to obtain. 👁 Add the title and subtitle
  4. Now, click on View App in the top right corner of the screen and select Staging Preview. 👁 Preview the app
  5. You can now view the Azure Data Lake Storage data in the app created on Adalo. 👁 View Azure Data Lake Storage table data in the form of a list in Adalo app

Live connections to Azure Data Lake Storage data from cloud applications

Adalo now allows you to connect to live Azure Data Lake Storage data directly, allowing you to create more connections and apps without duplicating Azure Data Lake Storage data.

To get real-time data access to over 100 SaaS, big data, and NoSQL sources directly from your cloud applications, visit CData Connect AI.