Integrate Live Google Cloud Storage 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 Google Cloud Storage 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 Google Cloud Storage data into your ML model deployments.

CData Connect AI provides a pure SQL, cloud-to-cloud interface for Google Cloud Storage, allowing you to easily integrate with live Google Cloud Storage 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 Google Cloud Storage, leveraging server-side processing to quickly return Google Cloud Storage data.

Configure Google Cloud Storage Connectivity for Amazon SageMaker Canvas

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

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

2. Select "Google Cloud Storage" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Google Cloud Storage.

   Authenticate with a User Account

   You can connect without setting any connection properties for your user credentials. After setting InitiateOAuth to GETANDREFRESH, you are ready to connect.

   When you connect, the Google Cloud Storage OAuth endpoint opens in your default browser. Log in and grant permissions, then the OAuth process completes

   Authenticate with a Service Account

   Service accounts have silent authentication, without user authentication in the browser. You can also use a service account to delegate enterprise-wide access scopes.

   You need to create an OAuth application in this flow. See the Help documentation for more information. After setting the following connection properties, you are ready to connect:

   • InitiateOAuth: Set this to GETANDREFRESH.
   • OAuthJWTCertType: Set this to "PFXFILE".
   • OAuthJWTCert: Set this to the path to the .p12 file you generated.
   • OAuthJWTCertPassword: Set this to the password of the .p12 file.
   • OAuthJWTCertSubject: Set this to "*" to pick the first certificate in the certificate store.
   • OAuthJWTIssuer: In the service accounts section, click Manage Service Accounts and set this field to the email address displayed in the service account Id field.
   • OAuthJWTSubject: Set this to your enterprise Id if your subject type is set to "enterprise" or your app user Id if your subject type is set to "user".
   • ProjectId: Set this to the Id of the project you want to connect to.

   The OAuth flow for a service account then completes.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Google Cloud Storage 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 Google Cloud Storage 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 Google Cloud Storage 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 Google Cloud Storage connection (e.g., GoogleCloudStorage1)
9. Click on "Create connection".

Integrating Google Cloud Storage Data into Amazon SageMaker Canvas

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

1. In the tabular dataset created in RDS with Google Cloud Storage data, search for the Google Cloud Storage connection configured on Connect AI in the search bar or from the list of connections.
2. Select the table of your choice from Google Cloud Storage, drag and drop it into the canvas on the right.
3. You can create workflows by joining any number of tables from the Google Cloud Storage 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 Google Cloud Storage 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 Google Cloud Storage Data from Cloud Applications

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