AWS Big Data Blog

Build governance dashboards for Amazon SageMaker Catalog with Amazon Quick

Maintaining visibility into your data catalog’s health requires more than ad-hoc queries. Data stewards and compliance teams need automated dashboards that surface governance metrics and alert them when issues arise. These issues include undocumented assets, missing ownership, and stale metadata.

In a previous post, we showed you how to query Amazon SageMaker Catalog metadata using SQL by using the metadata export feature. This post builds on that foundation by demonstrating how to create governance dashboards with Amazon Quick.

Amazon Quick is an agentic AI-powered digital workspace that provides integrated analytics, automation, and research capabilities. With Amazon Quick Sight, a component of Amazon Quick, you can create interactive dashboards and visualizations with automatic chart suggestions and machine learning (ML) insights.

We walk through how to connect Amazon Quick Sight to your Amazon SageMaker Catalog metadata and build governance dashboards using natural language prompts.

Solution overview

This solution extends the metadata export architecture by adding a visualization layer:

  1. Amazon SageMaker Catalog exports asset metadata daily to Amazon Simple Storage Service (Amazon S3) Tables
  2. Amazon Athena queries the metadata using standard SQL
  3. Amazon Quick Sight connects to Athena for interactive dashboards
  4. Amazon Quick uses natural language to build visualizations

AWS Cloud architecture diagram showing the data flow for SageMaker Catalog metadata visualization. Amazon SageMaker Catalog exports asset metadata and daily exports to Amazon S3 Tables in a bucket named aws-sagemaker-catalog. Amazon Athena queries the S3 Tables data using SQL queries. Amazon Quick connects to Athena to provide interactive dashboards, natural language queries, and executive summaries. Arrows indicate the left-to-right data flow from SageMaker Catalog to S3 Tables to Athena, with Athena connecting down to Amazon Quick.

Figure 1 – Amazon SageMaker Catalog governance dashboard architecture

Prerequisites

Before you begin, complete the following steps from Analyzing your data catalog: Query SageMaker Catalog metadata with SQL. You must also have the following:

  • Amazon SageMaker Catalog metadata export enabled
  • Amazon Athena configured with query results S3 bucket
  • AWS Lake Formation permissions configured for AWS Identity and Access Management (IAM)-based access
  • Verified that the asset_metadata.asset table contains data

Additionally, you need:

Building a governance dashboard with Amazon Quick Sight

To visualize catalog health metrics, connect Amazon Quick Sight to your Athena metadata tables.

Configure Amazon Quick Sight permissions

  1. Grant permissions to the Amazon Quick Sight service role.

The Amazon Quick Sight service role (default name: aws-quicksight-service-role-v0) needs permissions to access Amazon S3 Tables and AWS Glue catalog:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3tables:GetTableBucket",
        "s3tables:GetTable",
        "s3tables:GetTableMetadataLocation"
      ],
      "Resource": "arn:aws:s3tables:REGION:ACCOUNT_ID:bucket/aws-sagemaker-catalog/*"
    },
    {
      "Effect": "Allow",
      "Action": "glue:GetCatalog",
      "Resource": "arn:aws:glue:REGION:ACCOUNT_ID:catalog"
    }
  ]
}

Add this as an inline policy to the Amazon Quick Sight service role in the IAM console.

  1. Grant AWS Lake Formation permissions:

Both the Amazon Quick Sight service role and your Amazon Quick Sight admin user need AWS Lake Formation permissions on the S3 Tables catalog. First, find your Amazon Quick Sight admin user ARN by running this AWS Command Line Interface (AWS CLI) command:

aws quicksight list-users \
  --aws-account-id ACCOUNT_ID \
  --namespace default \
  --region us-east-1

Amazon Quick Sight users are managed in the Amazon Quick Sight home AWS Region (us-east-1).To grant permissions, use the Lake Formation console.

  1. Navigate to AWS Lake Formation in the AWS Management Console.
  2. Select Data permissions and Grant.
  3. For Principals, choose SAML users and groups.
  4. Enter your Amazon Quick Sight admin user ARN (from the preceding command).
  5. Under LF-Tags or catalog resources, choose Named Data Catalog resources.
  6. For Catalogs, choose the S3 Tables catalog: ACCOUNT_ID:s3tablescatalog/aws-sagemaker-catalog.
  7. For Databases, choose asset_metadata.
  8. Under Tables, choose asset.
  9. For Table permissions, choose Select and Describe.
  10. Select Grant.

Screenshot of AWS Lake Formation Grant permissions page showing the complete permission configuration workflow. At the top, the resource selection shows the 'asset_metadata' database and 'asset' table from the s3tablescatalog/aws-sagemaker-catalog catalog. Below that are optional sections for Views and Data filters, both unselected. The main content area displays three permission configuration sections. First, the 'Table permissions' section shows two subsections: 'Table permissions' with checkboxes for Select (checked, highlighted with orange box), Describe (checked, highlighted with orange box), Insert, Alter, Delete, Drop, and Super; and 'Grantable permissions' with the same permission options all unchecked. The Super permission includes explanatory text stating it is the union of all individual permissions and supersedes them. The Grantable permissions section explains that this allows the principal to grant any of the permissions to others and supersedes grantable permissions. At the bottom, the 'Data permissions' section displays two radio button options: 'All data access' (selected) which grants access to all data without restrictions, and 'Column-based access' which grants data access to specific columns only. An orange arrow points from the right side down to the bottom right corner where 'Cancel' and 'Grant' buttons are located, with the Grant button highlighted in orange.

Figure 2 – Grant access to Amazon SageMaker Catalog resources

  1. Repeat steps 1–9 for the Amazon Quick Sight service role, but in step 2 choose IAM users and roles instead.

When choosing the catalog in the Lake Formation console, you must choose the full S3 Tables catalog identifier (ACCOUNT_ID:s3tablescatalog/aws-sagemaker-catalog) to see the asset_metadata database.

Create an Amazon Quick Sight dataset.

Access S3 Tables data by creating a Quick Sight dataset using an Amazon Athena data source and the custom SQL option. An S3 Tables data source is also available but requires additional permissions. See Introducing new data source with S3 Tables in Amazon Quick for using S3 Tables as an Amazon Quick data source.

  1. Open Amazon Quick Sight in the AWS Management Console.
  2. Select Analyses and Create analysis.

Amazon QuickSight Analyses page showing the left navigation menu with Analyses selected under the Quick Sight section. The main content area displays a promotional banner for creating insightful and interactive visualizations with sample chart previews. Below the banner, an orange arrow points to the Create analysis button in the upper right. A table lists an existing analysis named New custom SQL analysis owned by Me and last updated a month ago.

Figure 3 – Create Amazon Quick Sight analysis

  1. Choose Create dataset and Create data source.

Amazon QuickSight Create Analysis dialog prompting the user to choose a dataset. A search field for datasets is shown at the top left. An orange arrow points to the Create dataset button in the upper right. A table below lists one available dataset named New custom SQL with a data source of New custom SQL, owned by Me, and last modified on March 5, 2026.

Figure 4 – Create dataset

  1. Select Amazon Athena as the data source and select Next.
  2. Enter a Data source name (for example, “SageMaker Catalog Metadata”) and choose Create data source.

Amazon QuickSight New Amazon Athena data source configuration dialog. The Data source name field is highlighted with an orange box and contains the value SageMaker Catalog Metadata. The Athena workgroup dropdown is set to primary. A Validate connection button and SSL is enabled label appear at the bottom left. An orange box highlights the Create data source button at the bottom right.

Figure 5 – Create data source

  1. Select Use custom SQL and enter a custom SQL query that references the S3 Tables catalog using the full three-part name.

Amazon QuickSight Choose your table dialog for the SageMaker Catalog Metadata data source. The Catalog dropdown is set to AwsDataCatalog and the Database dropdown shows a Select prompt. An instructional message explains to choose Prepare data to create a SQL query or choose Select table. An orange arrow points down to the Use custom SQL button highlighted with a blue box at the bottom center. The Select button is highlighted with an orange box at the bottom right.

Figure 6 – Use custom SQL

Amazon QuickSight Enter custom SQL query dialog. The query name field shows New custom SQL. The SQL editor contains a query reading SELECT FROM s3tablescatalog/aws-sagemaker-catalog with the query text underlined in orange. An orange box highlights the Confirm query button at the bottom right. An Edit/Preview data button appears at the bottom left.

Figure 7 – Enter custom SQL

SELECT * FROM "s3tablescatalog/aws-sagemaker-catalog".asset_metadata.asset

  1. Select Confirm query.
  2. Choose Directly query your data (SPICE import may fail with S3 Tables catalogs)
    Amazon QuickSight Finish dataset creation dialog showing the custom SQL dataset named New custom SQL with the SageMaker Catalog Metadata data source. Two radio button options are displayed: Import to SPICE for quicker analytics with 100 GB available shown in green, and Directly query your data which is selected and highlighted with an orange box. An orange box highlights the Visualize button at the bottom right. Edit/Preview data and Augment with SageMaker buttons appear at the bottom left and center.

Figure 8 – Directly query your data

  1. Choose Visualize and Create to start building your dashboard.

Create visualizations with Amazon Quick.

With Amazon Quick, you can build governance dashboards using natural language prompts. This removes the need for manual field configuration. This approach is faster and more intuitive than traditional dashboard building.The Amazon Quick Sight user must have AdminPro or AuthorPro subscription (the Build feature isn’t available for Reader users).Start building your dashboard with the following steps:

  1. Select Build in the top toolbar to open the natural language builder.

Amazon QuickSight analysis editor for New custom SQL analysis. The left Data panel shows the dataset fields including accountid, asset_created_time, asset_id, asset_name, asset_updated_time, business_description, catalog, extended_metadata, namespace, region, resource_description, resource_id, resource_name, resource_type_enum, and snapshot_time. The center Visuals panel shows the Build button highlighted with an orange box and a grid of available chart types. The right canvas area displays an empty AutoGraph placeholder with the message Add 1 or more fields to build a visual. An Add Data section with a dashed border prompts to add a dimension or measure.

Figure 9 – Amazon Quick build dashboard

  1. You will see a text box where you can describe the visualization that you want to create.

Amazon QuickSight analysis editor with the Build a visual panel open on the right side. An orange arrow points to the natural language input field where the user has typed a prompt requesting asset distribution by resource type as a pie chart, with a Build button next to it. Below the input field, a tooltip explains to describe the visual you would like to build with examples including map showing the top 5 cities by sales, MoM profit in 2026, and average revenue by quarter. The left Data panel shows dataset fields and the center Visuals panel displays available chart types.

Create each visualization using natural language. For each of the six recommended visualizations, enter the corresponding natural language prompt, select Build, then choose ADD TO ANALYSIS.

Amazon QuickSight analysis editor with the Build a visual panel open on the right side. The natural language prompt reads Show asset distribution by resource type as a pie chart with a Build button. Below, the system shows the interpretation as Unique number of Asset Id by Resource Type Enum using the New custom SQL dataset. A pie chart preview is displayed showing the distribution with a large segment labeled GlueTable. An orange arrow points to the Add to Analysis button at the bottom of the panel.

Figure 11 – Add to analysis

Visualization 1: Asset inventory by type

Show count of asset_id by resource_type_enum as a pie chart

After the pie chart is created, choose ADD TO ANALYSIS.

Visualization 2: Documentation completeness

Show count of asset_id where business_description is not null asa KPI

After the KPI is created, choose ADD TO ANALYSIS.

Visualization 3: Monthly registration trends

Show count of asset_id by asset_created_time month as a line chart

After the line chart is created, choose ADD TO ANALYSIS.

Visualization 4: Asset count by account

Show count of asset_id by account_id as a bar chart

After the bar chart is created, choose ADD TO ANALYSIS.

Visualization 5: Namespace distribution

Show count of asset_id by namespace as a treemap

After the treemap is created, choose ADD TO ANALYSIS.

Visualization 6: Resource type by namespace

Show count of asset_id by resource_type_enum and namespace as a heat map

Choose ADD TO ANALYSIS

  1. Arrange and publish your governance dashboard with the following steps:
  2. Delete any empty or unwanted visualizations by choosing the three dots menu and choosing Delete.
  3. Arrange visualizations by dragging them into your preferred layout.
  4. Resize visualizations to emphasize key metrics.
  5. Add titles to each visualization for clarity.
  6. Choose PUBLISH in the top right corner.
  7. Enter a dashboard name: “SageMaker Catalog Governance Dashboard”.
  8. Verify these options are selected:
    1. Allow executive summary.
    2. Allow sharing stories.
    3. Allow sharing scenarios.
  9. Choose Publish dashboard.

Amazon QuickSight SageMaker Catalog Governance Dashboard showing five visualizations. Top left is a pie chart titled Unique number of Asset Id by Resource Type showing all assets as GlueTable type. Top center is a key performance indicator displaying a total of 500 unique assets. Top right is a horizontal bar chart titled Unique number of Asset Id by Account Id showing five AWS account IDs with values of 109, 105, 104, 103, and 79 assets respectively. Middle left is a stacked bar chart titled Unique number of Asset Id by Resource Type Enum and Namespace showing GlueTable assets distributed across namespaces with values ranging from 33 to 52. Middle right is a treemap titled Unique number of Asset Id by Namespace with trading_analytics at 52, compliance_reporting at 51, treasury_ops at 50, market_data at 44, fraud_detection at 42, customer_analytics at 40, credit_scoring at 40, risk_management at 39, portfolio_mgmt at 37, regulatory at 37, loan_origination at 35, and payments at 33. Bottom is a line chart titled Unique number of Asset Id by Asset Created Time month showing asset creation trends from April 2025 through March 2026 with values fluctuating between approximately 30 and 50 assets per month.

Figure 12 – Amazon SageMaker Catalog governance dashboard

    1. Analyze your dashboard with natural language.

After you publish, you can ask questions about your governance data:

    1. On the dashboard, choose Analyze this dashboard in a Scenario in the top center.
    2. In the Data to Insights panel, enter natural language questions such as:
      1. “Which resource types have the lowest documentation rates?”
      2. “How many assets were registered last month compared to this month?”
      3. “What percentage of assets lack ownership information?”
    3. Choose Submit to generate AI-powered insights.

Amazon Quick analyzes your data and provides insights with supporting visualizations.

    1. Generate executive summaries

Create automated governance reports for data stewards and compliance teams:

    1. Choose the Amazon Quick logo in the top left to return to the home page
    2. Select Dashboards from the left panel
    3. Choose your “SageMaker Catalog Governance Dashboard”
    4. Choose the Create dropdown menu in the top right
    5. Select Executive Summary

Amazon Quick will automatically generate a summary with key governance insights, including Total asset counts and growth trends, Documentation completeness metrics, Ownership coverage statistics, and Classification distribution analysis.

    1. Create governance stories.

Build governance reports that combine multiple dashboards:

    1. From the Create dropdown, select Story.
    2. Enter a prompt: “Write a summary of catalog governance metrics and data quality trends”.
    3. Choose Add to select dashboards to include in the report.
    4. Choose Build (this might take a few minutes to complete).

Amazon Quick will generate a narrative report combining your visualizations with AI-generated insights. Share the reports with leadership or compliance teams.

Governance dashboards contain metadata such as ownership and classification details. Restrict access to users who need it. In the Amazon Quick Sight console, open the dashboard, choose Share, and grant access to named users or a dedicated Quick Sight group (for example, data-stewards) instead of selecting Everyone in this account. Review the dashboard’s permissions periodically and remove entries that are no longer needed.

Cleaning up

To avoid ongoing charges, clean up the resources created in this walkthrough. Delete Amazon Quick Sight resources including the dashboard, analyses, and dataset.

Conclusion

In this post, you connected Amazon Quick Sight to your Amazon SageMaker Catalog metadata export, built governance dashboards using the Amazon Quick natural language prompts. This approach gives data stewards and compliance teams visibility into catalog health through six key visualizations covering asset inventory, documentation completeness, registration trends, account distribution, classification coverage, and stale asset detection.

Together with the metadata export and SQL query capabilities covered in the Analyzing your data catalog: Query SageMaker Catalog metadata with SQL post, this solution provides a complete, low-overhead governance monitoring pipeline from raw catalog metadata to executive-ready.

To learn more about Amazon SageMaker Catalogs, see Amazon SageMaker Catalog documentation. To expand the work done with Amazon Quick, review Amazon Quick Sight documentation.

About the authors

Steve Phillips

Steve is a Principal Technical Account Manager and Analytics specialist at AWS in the North America region. Steve currently focuses on data warehouse architectural design, data lakes, data ingestion pipelines, and cloud distributed architectures.

Ramesh Singh

Ramesh is a Senior Product Manager Technical (External Services) at AWS in Seattle, Washington, currently with the Amazon SageMaker team. He is passionate about building high-performance ML/AI and analytics products that help enterprise customers achieve their critical goals using cutting-edge technology.

Pradeep Misra

Pradeep is a Principal Analytics and Applied AI Solutions Architect at AWS. He is passionate about solving customer challenges using data, analytics, and Applied AI. Outside of work, he likes exploring new places and playing badminton with his family. He also likes doing science experiments, building LEGOs, and watching anime with his daughters.

Rohith Kayathi

Rohith is a Senior Software Engineer at Amazon Web Services (AWS) working with Amazon SageMaker team. He leads business data catalog, generative AI–powered metadata curation, and lineage solutions. He is passionate about building large-scale distributed systems, solving complex problems, and setting the bar for engineering excellence for his team.