Integrate Live SAS Data Sets 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 SAS Data Sets 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 SAS Data Sets data into your ML model deployments.

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

Configure SAS Data Sets Connectivity for Amazon SageMaker Canvas

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

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

2. Select "SAS Data Sets" from the Add Connection panel

3. Enter the necessary authentication properties to connect to SAS Data Sets.

   Set the following connection properties to connect to your SAS DataSet files:

   Connecting to Local Files

   • Set the Connection Type to "Local." Local files support SELECT, INSERT, and DELETE commands.
   • Set the URI to a folder containing SAS files, e.g. C:\PATH\TO\FOLDER\.

   Connecting to Cloud-Hosted SAS DataSet Files

   While the driver is capable of pulling data from SAS DataSet files hosted on a variety of cloud data stores, INSERT, UPDATE, and DELETE are not supported outside of local files in this driver.

   Set the Connection Type to the service hosting your SAS DataSet files. A unique prefix at the beginning of the URI connection property is used to identify the cloud data store and the remainder of the path is a relative path to the desired folder (one table per file) or single file (a single table). For more information, refer to the Getting Started section of the Help documentation.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add SAS Data Sets 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 SAS Data Sets 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 SAS Data Sets 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 SAS Data Sets connection (e.g., SASDataSets1)
9. Click on "Create connection".

Integrating SAS Data Sets Data into Amazon SageMaker Canvas

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

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

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