Integrate Live Confluence 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 Confluence 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 Confluence data into your ML model deployments.

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

Configure Confluence Connectivity for Amazon SageMaker Canvas

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

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

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

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

   Obtaining an API Token

   An API token is necessary for account authentication. To generate one, login to your Atlassian account and navigate to API tokens > Create API token. The generated token will be displayed.

   Connect Using a Confluence Cloud Account

   To connect to a Cloud account, provide the following (Note: Password has been deprecated for connecting to a Cloud Account and is now used only to connect to a Server Instance.):

   • User: The user which will be used to authenticate with the Confluence server.
   • APIToken: The API Token associated with the currently authenticated user.
   • Url: The URL associated with your JIRA endpoint. For example, https://yoursitename.atlassian.net.

   Connect Using a Confluence Server Instance

   To connect to a Server instance, provide the following:

   • User: The user which will be used to authenticate with the Confluence instance.
   • Password: The password which will be used to authenticate with the Confluence server.
   • Url: The URL associated with your JIRA endpoint. For example, https://yoursitename.atlassian.net.
   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Confluence 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 Confluence 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 Confluence 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 Confluence connection (e.g., Confluence1) 👁 Create an RDS connection
     
9. Click on "Create connection".

Integrating Confluence Data into Amazon SageMaker Canvas

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

1. In the tabular dataset created in RDS with Confluence data, search for the Confluence connection configured on Connect AI in the search bar or from the list of connections. 👁 Search for the Confluence connection
   
2. Select the table of your choice from Confluence, 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 Confluence 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 Confluence 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 Confluence Data from Cloud Applications

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