1. Collaborative AI Data Analysis using AWS QuickSight Sharing Features


    In order to facilitate a collaborative AI data analysis using AWS QuickSight, you would typically start by creating an analysis in QuickSight and then share it with other users. This sharing can be controlled through permissions, allowing specific actions to be performed by the collaborators.

    To automate this setup with Pulumi, you can use AWS QuickSight resources. The critical resources for this task are:

    • Analysis: Represents an analysis in AWS QuickSight that consists of a combination of data sets, visuals, and sheets.
    • Namespace: Serve as containers for assets in QuickSight that help manage QuickSight users and groups.

    Here's how you'd create an analysis in QuickSight and grant permissions using Pulumi in Python:

    1. Create a QuickSight dataset if it doesn't already exist. Your analysis will be built on top of these datasets.
    2. Create a QuickSight analysis resource. You'll specify the datasets to use, along with any parameters and theme configuration.
    3. Define permissions for the analysis to specify which users or groups can access it and what actions they can perform.

    Below is a Pulumi program in Python that demonstrates these steps. This program assumes that the AWS provider and configuration have been set up beforehand.

    import pulumi import pulumi_aws as aws # Define a QuickSight dataset that your analysis will be based on # This is a placeholder resource to represent an existing dataset. # In a real scenario, you would define the dataset here with all the required configurations. # Create the QuickSight Analysis resource quicksight_analysis = aws.quicksight.Analysis("aiDataAnalysis", aws_account_id=aws.config.account_id, # Your AWS Account ID analysis_id="my-analysis-id", # Provide a unique identifier for the analysis source_entity={ "sourceTemplate": { "arn": "arn:aws:quicksight:REGION:ACCOUNT-ID:template/TEMPLATE-ID", # Replace with the correct ARN for your template "dataSetReferences": [ { "dataSetArn": "arn:aws:quicksight:REGION:ACCOUNT-ID:dataset/DATASET-ID", # Replace with the correct ARN for your dataset "dataSetPlaceholder": "placeholder" # Name of the placeholder that this dataset will be replacing in the analysis } ] } }, permissions=[ { "Principal": "arn:aws:quicksight:REGION:ACCOUNT-ID:user/NAMESPACE/USER-NAME", # Replace with principal's ARN "actions": [ # Actions Definition, "quicksight:DescribeAnalysis", "quicksight:DescribeAnalysisPermissions", "quicksight:UpdateAnalysisPermissions", "quicksight:UpdateAnalysis", # Add additional actions as needed based on your collaboration requirements ] }, ], theme_arn="arn:aws:quicksight:REGION:ACCOUNT-ID:theme/THEME-ID", # Replace with the correct ARN for your theme, if any tags={ "Name": "AiDataAnalysis", "Project": "CollaborationProject" } ) # Output the Analysis URL, which can be used for accessing the QuickSight analysis in a web browser pulumi.export("analysis_url", quicksight_analysis.arn.apply( lambda arn: f"https://{aws.config.region}.quicksight.aws.amazon.com/sn/analyses/{arn.split('/')[5]}" ))

    This program sets up a new QuickSight Analysis using an existing dataset and template. It assigns permissions for a given principal to define what actions they can execute on the analysis. Lastly, it exports the URL to access the analysis.

    Understanding the properties:

    • aws_account_id: This should be the AWS account ID in which you want to create the QuickSight analysis.
    • analysis_id: A unique identifier for the QuickSight analysis you're creating.
    • source_entity: This property specifies the source of the analysis, such as a template that includes a dataset.
    • permissions: A list of permissions that grant a principal (user or group) different actions they can take on the analysis.
    • theme_arn: An ARN identifier for a specific QuickSight theme to style the analysis.
    • tags: Key-value pairs associated with the resource, used for organizing and managing resources.

    Keep in mind that this is a simplified example. When creating such a resource for real-world usage, you'll want to replace placeholders with actual values like ARN, ACCOUNT-ID, REGION, NAMESPACE, and USER-NAME. Additionally, you should configure source_entity and permissions based on your specific analysis template, datasets, and collaboration requirements.