Integrate Live Jira Service Management 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 Jira Service Management 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 Jira Service Management data into your ML model deployments.

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

Configure Jira Service Management Connectivity for Amazon SageMaker Canvas

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

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

2. Select "Jira Service Management" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Jira Service Management.

   You can establish a connection to any Jira Service Desk Cloud account or Server instance.

   Connecting with a Cloud Account

   To connect to a Cloud account, you'll first need to retrieve an APIToken. To generate one, log in to your Atlassian account and navigate to API tokens > Create API token. The generated token will be displayed.

   Supply the following to connect to data:

   • User: Set this to the username of the authenticating user.
   • APIToken: Set this to the API token found previously.

   Connecting with a Service Account

   To authenticate with a service account, supply the following connection properties:

   • User: Set this to the username of the authenticating user.
   • Password: Set this to the password of the authenticating user.
   • URL: Set this to the URL associated with your JIRA Service Desk endpoint. For example, https://yoursitename.atlassian.net.

   Note: Password has been deprecated for connecting to a Cloud Account and is now used only to connect to a Server Instance.

   Accessing Custom Fields

   By default, the connector only surfaces system fields. To access the custom fields for Issues, set IncludeCustomFields.

   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Jira Service Management 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 Jira Service Management 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 Jira Service Management 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 Jira Service Management connection (e.g., JiraServiceDesk1) 👁 Create an RDS connection
     
9. Click on "Create connection".

Integrating Jira Service Management Data into Amazon SageMaker Canvas

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

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

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