Integrate Live Amazon Athena 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 Amazon Athena 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 Amazon Athena data into your ML model deployments.

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

Getting Started

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Configure Amazon Athena Connectivity for Amazon SageMaker Canvas

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

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

2. Select "Amazon Athena" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Amazon Athena.

   Authenticating to Amazon Athena

   To authorize Amazon Athena requests, provide the credentials for an administrator account or for an IAM user with custom permissions: Set to the access key Id. Set to the secret access key.

   Note: Though you can connect as the AWS account administrator, it is recommended to use IAM user credentials to access AWS services.

   Obtaining the Access Key

   To obtain the credentials for an IAM user, follow the steps below:

   1. Sign into the IAM console.
   2. In the navigation pane, select Users.
   3. To create or manage the access keys for a user, select the user and then select the Security Credentials tab.

   To obtain the credentials for your AWS root account, follow the steps below:

   1. Sign into the AWS Management console with the credentials for your root account.
   2. Select your account name or number and select My Security Credentials in the menu that is displayed.
   3. Click Continue to Security Credentials and expand the Access Keys section to manage or create root account access keys.

   Authenticating from an EC2 Instance

   If you are using the CData Data Provider for Amazon Athena 2018 from an EC2 Instance and have an IAM Role assigned to the instance, you can use the IAM Role to authenticate. To do so, set to true and leave and empty. The CData Data Provider for Amazon Athena 2018 will automatically obtain your IAM Role credentials and authenticate with them.

   Authenticating as an AWS Role

   In many situations it may be preferable to use an IAM role for authentication instead of the direct security credentials of an AWS root user. An AWS role may be used instead by specifying the . This will cause the CData Data Provider for Amazon Athena 2018 to attempt to retrieve credentials for the specified role. If you are connecting to AWS (instead of already being connected such as on an EC2 instance), you must additionally specify the and of an IAM user to assume the role for. Roles may not be used when specifying the and of an AWS root user.

   Authenticating with MFA

   For users and roles that require Multi-factor Authentication, specify the and connection properties. This will cause the CData Data Provider for Amazon Athena 2018 to submit the MFA credentials in a request to retrieve temporary authentication credentials. Note that the duration of the temporary credentials may be controlled via the (default 3600 seconds).

   Connecting to Amazon Athena

   In addition to the and properties, specify , and . Set to the region where your Amazon Athena data is hosted. Set to a folder in S3 where you would like to store the results of queries.

   If is not set in the connection, the data provider connects to the default database set in Amazon Athena.

   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Amazon Athena 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.

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. 👁 Creating a new PAT
   
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 Amazon Athena 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 Amazon Athena 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". 👁 Open SageMaker Canvas application
   
2. Once the Canvas application opens, navigate to the left panel, and select "My models". 👁 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". 👁 Create a new model
   
5. Once the model version gets created, click on "Create dataset" in the Select dataset tab. 👁 Select a dataset
   
6. In the Create a tabular dataset window, add a "Dataset name" and click on "Create". 👁 Create a tabular dataset
   
7. Click on the "Data Source" drop-down and search for or navigate to the RDS connector and click on " Add Connection". 👁 Select RDS connector
   
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 Amazon Athena connection (e.g., AmazonAthena1) 👁 Create an RDS connection
     
9. Click on "Create connection".

Integrating Amazon Athena Data into Amazon SageMaker Canvas

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

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

At this point, you have access to live Amazon Athena 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 Amazon Athena Data from Cloud Applications

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