Integrate Live JSON Services 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 JSON services 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 JSON services into your ML model deployments.

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

Configure JSON Connectivity for Amazon SageMaker Canvas

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

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

2. Select "JSON" from the Add Connection panel

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

   See the Getting Started chapter in the data provider documentation to authenticate to your data source: The data provider models JSON APIs as bidirectional database tables and JSON files as read-only views (local files, files stored on popular cloud services, and FTP servers). The major authentication schemes are supported, including HTTP Basic, Digest, NTLM, OAuth, and FTP. See the Getting Started chapter in the data provider documentation for authentication guides.

   After setting the URI and providing any authentication values, set DataModel to more closely match the data representation to the structure of your data.

   The DataModel property is the controlling property over how your data is represented into tables and toggles the following basic configurations.

   • Document (default): Model a top-level, document view of your JSON data. The data provider returns nested elements as aggregates of data.
   • FlattenedDocuments: Implicitly join nested documents and their parents into a single table.
   • Relational: Return individual, related tables from hierarchical data. The tables contain a primary key and a foreign key that links to the parent document.

   See the Modeling JSON Data chapter for more information on configuring the relational representation. You will also find the sample data used in the following examples. The data includes entries for people, the cars they own, and various maintenance services performed on those cars.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add JSON 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 JSON services 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 JSON services 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 JSON connection (e.g., JSON1)
9. Click on "Create connection".

Integrating JSON Services into Amazon SageMaker Canvas

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

1. In the tabular dataset created in RDS with JSON services, search for the JSON connection configured on Connect AI in the search bar or from the list of connections.
2. Select the table of your choice from JSON, drag and drop it into the canvas on the right.
3. You can create workflows by joining any number of tables from the JSON 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 JSON services in Amazon SageMaker that you can utilize to build custom ML models to generate predictive business insights and grow your organization.

SQL Access to JSON Services from Cloud Applications

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

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.