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 SingleStore 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 SingleStore data into your ML model deployments.
CData Connect AI provides a pure SQL, cloud-to-cloud interface for SingleStore, allowing you to easily integrate with live SingleStore 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 SingleStore, leveraging server-side processing to quickly return SingleStore data.
Configure SingleStore Connectivity for Amazon SageMaker Canvas
Connectivity to SingleStore from Amazon SageMaker Canvas is made possible through CData Connect AI. To work with SingleStore data from Amazon SageMaker Canvas, we start by creating and configuring a SingleStore connection.
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Log into Connect AI, click Sources, and then click Add Connection
👁 Adding a Connection
- Select "SingleStore" from the Add Connection panel
👁 Selecting a data source
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Enter the necessary authentication properties to connect to SingleStore.
The following connection properties are required in order to connect to data.
- Server: The host name or IP of the server hosting the SingleStore database.
- Port: The port of the server hosting the SingleStore database.
- Database (Optional): The default database to connect to when connecting to the SingleStore Server. If this is not set, tables from all databases will be returned.
Connect Using Standard Authentication
To authenticate using standard authentication, set the following:
- User: The user which will be used to authenticate with the SingleStore server.
- Password: The password which will be used to authenticate with the SingleStore server.
Connect Using Integrated Security
As an alternative to providing the standard username and password, you can set IntegratedSecurity to True to authenticate trusted users to the server via Windows Authentication.
Connect Using SSL Authentication
You can leverage SSL authentication to connect to SingleStore data via a secure session. Configure the following connection properties to connect to data:
- SSLClientCert: Set this to the name of the certificate store for the client certificate. Used in the case of 2-way SSL, where truststore and keystore are kept on both the client and server machines.
- SSLClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
- SSLClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
- SSLClientCertType: The certificate type of the client store.
- SSLServerCert: The certificate to be accepted from the server.
Connect Using SSH Authentication
Using SSH, you can securely login to a remote machine. To access SingleStore data via SSH, configure the following connection properties:
- SSHClientCert: Set this to the name of the certificate store for the client certificate.
- SSHClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
- SSHClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
- SSHClientCertType: The certificate type of the client store.
- SSHPassword: The password that you use to authenticate with the SSH server.
- SSHPort: The port used for SSH operations.
- SSHServer: The SSH authentication server you are trying to authenticate against.
- SSHServerFingerPrint: The SSH Server fingerprint used for verification of the host you are connecting to.
- SSHUser: Set this to the username that you use to authenticate with the SSH server.
👁 Configuring a connection (Salesforce is shown)
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Click Save & Test
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Navigate to the Permissions tab in the Add SingleStore 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.
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Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
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On the Settings page, go to the Access Tokens section and click Create PAT.
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Give the PAT a name and click Create.
👁 Creating a new PAT
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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 SingleStore 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 SingleStore data into Amazon SageMaker Canvas using its RDS connector.
- Select a domain and user profile in Amazon SageMaker Canvas and click on "Open Canvas".
👁 Open SageMaker Canvas application
- Once the Canvas application opens, navigate to the left panel, and select "My models".
👁 Select My models
- Click on "Create new model" in the My models screen.
- Specify a Model name in Create new model window and select a Problem type. Click on "Create".
👁 Create a new model
- Once the model version gets created, click on "Create dataset" in the Select dataset tab.
👁 Select a dataset
- In the Create a tabular dataset window, add a "Dataset name" and click on "Create".
👁 Create a tabular dataset
- Click on the "Data Source" drop-down and search for or navigate to the RDS connector and click on " Add Connection".
👁 Select RDS connector
- 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 SingleStore connection (e.g., SingleStore1)
👁 Create an RDS connection
- Click on "Create connection".
Integrating SingleStore Data into Amazon SageMaker Canvas
With the connection to Connect AI configured in the RDS, you are ready to integrate live SingleStore data into your Amazon SageMaker Canvas dataset.
- In the tabular dataset created in RDS with SingleStore data, search for the SingleStore connection configured on Connect AI in the search bar or from the list of connections.
👁 Search for the SingleStore connection
- Select the table of your choice from SingleStore, drag and drop it into the canvas on the right.
👁 Select a table of your choice
- You can create workflows by joining any number of tables from the SingleStore connection (as shown below). Click on "Create dataset".
👁 Create the workflow and the dataset
- Once the dataset is created, click on "Select dataset" to build your model.
👁 Select the dataset to build a model
👁 Build a model from the dataset
- Perform analysis, generate prediction, and deploy the model.
At this point, you have access to live SingleStore 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 SingleStore Data from Cloud Applications
Now you have a direct connection to live SingleStore data from Amazon SageMaker Canvas. You can create more connections, datasets, and predictive models to drive business — all without replicating SingleStore 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.
