Build Azure Data Lake Storage-Connected Apps in Bubble

Bubble is a no-code platform that simplifies the process of developing and launching apps and businesses. When coupled with CData Connect AI, you gain effortless no-code access to Azure Data Lake Storage data for streamlined app development. This article guides you through the integration process from Bubble to Azure Data Lake Storage using CData Connect AI.

CData Connect AI provides a pure cloud-to-cloud OData interface for Azure Data Lake Storage data that allows you to integrate from Bubble to Azure Data Lake Storage data in real time.

Connect to Azure Data Lake Storage from Bubble

To work with Azure Data Lake Storage data from Bubble, we need to connect to Azure Data Lake Storage from Connect AI, provide user access to the connection, and create a Workspace 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
👁 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.

Configure Azure Data Lake Storage Endpoints for Bubble

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 Bubble.

Build a Azure Data Lake Storage-Connected App

With OData endpoints added to Connect AI, you can build an app in Bubble with live access to Azure Data Lake Storage data.

Configure the Bubble API Connector

To start, Configure the API Connector for accessing CData Connect AI from Bubble.

1. In your app, click the Plugins tab and click Add plugins
2. Search for "API Connector" and click Install 👁 Installing the API Connector plugin
   
3. After installation, click the "Add another API" button and configure the API:
   • Name the API
   • Set Authentication to "HTTP Basic Auth"
   • Set Username to a Connect AI user (e.g. [email protected])
   • Set Password to the PAT for the user
   • Expand the API Call, select the "GET" command and set the URL to a Workspace endpoint you previously configured (e.g. https://cloud.cdata.com/api/odata/{workspace_name}/Resources)
   👁 Configuring the API Call
   
4. Click the "Initialize call" button to adjust data types in the response (as needed) 👁 Initializing the call
   
5. After making any necessary changes, click "Save" 👁 Saving the data types
   

Configure the App UI

With the API Connector configure, you are ready to retrieve Azure Data Lake Storage data in your Bubble app. In this article, we request the data with a UI component that can display an Excel-like table.

1. On the Plugin tab, install the "Excel-like HandsonTable" 👁 Adding the Excel-like HandsonTable plugin
   
2. On the Design tab, add an "Excel Table" to the workspace 👁 Adding an Excel Table to the app
   
3. In the Excel Table, in Data source, select "Get data from an external API"
4. Set Type of content to "API Call value"
5. Set Data source to "CData Connect AI - API Call's value" (or equivalent) 👁 Binding the Excel Table to the API Call
   
6. Click "Preview" to ensure the data was retrieved from Azure Data Lake Storage. 👁 Previewing the app
   

At this point, you can develop applications with live access to Azure Data Lake Storage data without needing to know the complexities of the back-end API.

Live Access to Azure Data Lake Storage Data from Cloud Applications

Now you have a direct connection to live Azure Data Lake Storage data from Bubble. You can create more Azure Data Lake Storage-connected apps — all without writing any code or replicating Azure Data Lake Storage data.

To get live data access to hundreds of SaaS, Big Data, and NoSQL sources directly from your cloud applications, see the CData Connect AI.