Integrate Live HubDB Data into Amazon SageMaker Canvas with RDS

Amazon SageMaker Canvas is a no-code machine learning platform that lets you generate predictions, prepare data, and build models without writing code. When paired with CData Connect AI, you get instant, cloud-to-cloud access to HubDB data for building custom machine-learning models, predicting customer churn, generating texts, building chatbots, and more. This article shows how to connect to Connect AI from Amazon SageMaker Canvas using the RDS connector and integrate live HubDB data into your ML model deployments.

CData Connect AI provides a pure SQL, cloud-to-cloud interface for HubDB, allowing you to easily integrate with live HubDB data in Amazon SageMaker Canvas — without replicating the data. CData Connect AI looks exactly like a SQL Server database to Amazon SageMaker Canvas and uses optimized data processing out of the box to push all supported SQL operations (filters, JOINs, etc) directly to HubDB, leveraging server-side processing to quickly return HubDB data.

Configure HubDB Connectivity for Amazon SageMaker Canvas

Connectivity to HubDB from Amazon SageMaker Canvas is made possible through CData Connect AI. To work with HubDB data from Amazon SageMaker Canvas, we start by creating and configuring a HubDB connection.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "HubDB" from the Add Connection panel

3. Enter the necessary authentication properties to connect to HubDB.

   There are two authentication methods available for connecting to HubDB data source: OAuth Authentication with a public HubSpot application and authentication with a Private application token.

   Using a Custom OAuth App

   AuthScheme must be set to "OAuth" in all OAuth flows. Be sure to review the Help documentation for the required connection properties for you specific authentication needs (desktop applications, web applications, and headless machines).

   Follow the steps below to register an application and obtain the OAuth client credentials:

   1. Log into your HubSpot app developer account.
      • Note that it must be an app developer account. Standard HubSpot accounts cannot create public apps.
   2. On the developer account home page, click the Apps tab.
   3. Click Create app.
   4. On the App info tab, enter and optionally modify values that are displayed to users when they connect. These values include the public application name, application logo, and a description of the application.
   5. On the Auth tab, supply a callback URL in the "Redirect URLs" box.
      • If you're creating a desktop application, set this to a locally accessible URL like http://localhost:33333.
      • If you are creating a Web application, set this to a trusted URL where you want users to be redirected to when they authorize your application.
   6. Click Create App. HubSpot then generates the application, along with its associated credentials.
   7. On the Auth tab, note the Client ID and Client secret. You will use these later to configure the driver.

   8. Under Scopes, select any scopes you need for your application's intended functionality.

      A minimum of the following scopes is required to access tables:

      • hubdb
      • oauth
      • crm.objects.owners.read
   9. Click Save changes.
   10. Install the application into a production portal with access to the features that are required by the integration.
       • Under "Install URL (OAuth)", click Copy full URL to copy the installation URL for your application.
       • Navigate to the copied link in your browser. Select a standard account in which to install the application.
       • Click Connect app. You can close the resulting tab.

   Using a Private App

   To connect using a HubSpot private application token, set the AuthScheme property to "PrivateApp."

   You can generate a private application token by following the steps below:

   1. In your HubDB account, click the settings icon (the gear) in the main navigation bar.
   2. In the left sidebar menu, navigate to Integrations > Private Apps.
   3. Click Create private app.
   4. On the Basic Info tab, configure the details of your application (name, logo, and description).
   5. On the Scopes tab, select Read or Write for each scope you want your private application to be able to access.
   6. A minimum of hubdb and crm.objects.owners.read is required to access tables.
   7. After you are done configuring your application, click Create app in the top right.
   8. Review the info about your application's access token, click Continue creating, and then Show token.
   9. Click Copy to copy the private application token.

   To connect, set PrivateAppToken to the private application token you retrieved.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add HubDB Connection page and update the User-based 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.
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 HubDB data from Amazon SageMaker Canvas.

Connecting to CData Connect AI from Amazon SageMaker Canvas

With the connection in CData Connect AI configured, you are ready to integrate live HubDB data into Amazon SageMaker Canvas using its RDS connector.

1. Select a domain and user profile in Amazon SageMaker Canvas and click on "Open Canvas".
2. Once the Canvas application opens, navigate to the left panel, and select "My models".
3. Click on "Create new model" in the My models screen.
4. Specify a Model name in Create new model window and select a Problem type. Click on "Create".
5. Once the model version gets created, click on "Create dataset" in the Select dataset tab.
6. In the Create a tabular dataset window, add a "Dataset name" and click on "Create".
7. Click on the "Data Source" drop-down and search for or navigate to the RDS connector and click on " Add Connection".
8. In the Add a new RDS connection window, set the following properties:
   
   
   • Connection Name: a relevant connection name
   • Set Engine type to sqlserver-web
   • Set Port to 14333
   • Set Address as tds.cdata.com
   • Set Username to a Connect AI user (e.g. [email protected])
   • Set Password to the PAT for the above user
   • Set Database name the HubDB connection (e.g., HubDB1)
9. Click on "Create connection".

Integrating HubDB Data into Amazon SageMaker Canvas

With the connection to Connect AI configured in the RDS, you are ready to integrate live HubDB data into your Amazon SageMaker Canvas dataset.

1. In the tabular dataset created in RDS with HubDB data, search for the HubDB connection configured on Connect AI in the search bar or from the list of connections.
2. Select the table of your choice from HubDB, drag and drop it into the canvas on the right.
3. You can create workflows by joining any number of tables from the HubDB connection (as shown below). Click on "Create dataset".
4. Once the dataset is created, click on "Select dataset" to build your model.
5. Perform analysis, generate prediction, and deploy the model.

At this point, you have access to live HubDB data in Amazon SageMaker that you can utilize to build custom ML models to generate predictive business insights and grow your organization.

SQL Access to HubDB Data from Cloud Applications

Now you have a direct connection to live HubDB data from Amazon SageMaker Canvas. You can create more connections, datasets, and predictive models to drive business — all without replicating HubDB data.

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