
Query AWS SageMaker Catalog
OfficialFreeRun SQL analytics on SageMaker Catalog metadata.
Free · Opens the source repo
What Query AWS SageMaker Catalog does
The Query AWS SageMaker Catalog skill enables users to perform SQL analytics on asset metadata exported from the Amazon SageMaker Catalog. This skill is particularly useful for those managing data governance, tracking asset growth, and auditing data ownership within their AWS environments. By leveraging the Apache Iceberg format in S3, users can access a read-only snapshot of their catalog data, allowing for efficient querying without the need for custom ETL processes.
With this skill, users can execute queries to analyze various aspects of their catalog, such as identifying assets without descriptions, tracking changes over time, and assessing the quality of metadata. The skill is designed to work seamlessly with the AWS MCP server, ensuring a secure and logged environment for executing commands. Users can perform governance queries, compare historical catalog states, and conduct audits to ensure compliance and data integrity.
The skill is ideal for data engineers, data scientists, and compliance officers who need to maintain oversight of their data assets and ensure that metadata is complete and accurate. By utilizing SQL syntax, users can easily filter and aggregate data based on their specific needs, making it a versatile tool for catalog management. The focus on governance and ownership audits makes it particularly valuable for organizations that prioritize data stewardship and compliance.
To get started, users need to ensure their AWS environment is configured correctly, including permissions for querying the catalog. The skill provides clear instructions for setting up the necessary configurations and permissions, making it accessible for users with varying levels of experience in AWS and SQL.
When to use it
Use this skill when you need to run SQL queries on catalog metadata for governance, audits, or historical comparisons.
When not to use it
This skill is not suitable for interactive browsing of catalog assets or for managing catalog metadata directly.
What you can build with it
Governance Audits
Run queries to identify assets without descriptions or owners, ensuring compliance with data governance policies.
Asset Growth Tracking
Analyze changes in asset counts over time to monitor data growth and usage trends.
Historical Comparisons
Utilize time-travel capabilities to compare catalog states from different snapshots, identifying changes and updates.
How to install Query AWS SageMaker Catalog
View source1. Install with the skills CLI
npx skills add aws/agent-toolkit-for-aws/querying-aws-sagemaker-catalog --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by awsQuery AWS SageMaker Catalog System Tables
Overview
Works best with the AWS MCP server for sandboxed execution and audit logging. All commands below use the AWS CLI and work in any environment with configured AWS credentials.
Amazon SageMaker Unified Studio (whose catalog feature is referred to below as SageMaker Catalog) exports asset metadata as a daily-snapshot
Apache Iceberg table in the AWS-managed aws-sagemaker-catalog table bucket. This
enables SQL queries over your entire data catalog inventory — asset counts, governance
gaps, ownership audits, and historical comparisons — without building custom ETL.
Data is partitioned by snapshot_time and exported once daily (around midnight per
region). The table is read-only.
Decision Tree
| User intent | Use this skill? | Alternative |
|---|---|---|
| SQL analytics on catalog state (counts, governance, trends) | Yes | — |
| Historical comparison ("what changed in catalog last week") | Yes — time travel via snapshot_time | — |
| Find assets without owners or descriptions | Yes | — |
| Find a specific table by name or concept | No | finding-data-lake-assets or Glue Discovery search |
| Browse/enumerate catalog interactively | No | exploring-data-catalog |
| Run a query on a table's data | No | querying-data-lake |
| Manage catalog metadata (add descriptions, tags) | No | Glue Discovery put-form-type / associate-glossary-terms |
Common Tasks
1. Check If Configured
aws datazone get-data-export-configuration \
--domain-identifier <DOMAIN_ID> \
--region <REGION>
- If no domain exists:
aws datazone list-domains --region <REGION> - If export not enabled: guide user to enable.
- One domain per account per region.
Verify table bucket exists:
aws s3tables list-table-buckets --region <REGION> \
--query "tableBuckets[?name=='aws-sagemaker-catalog']"
2. Enable
With KMS encryption (recommended for production):
aws datazone put-data-export-configuration \
--domain-identifier <DOMAIN_ID> \
--region <REGION> \
--enable-export \
--encryption-configuration kmsKeyArn=<KMS_KEY_ARN>,sseAlgorithm=aws:kms
Note: Encryption cannot be changed after creation. Always specify KMS for sensitive catalog data.
Without encryption (for quick testing only):
aws datazone put-data-export-configuration \
--domain-identifier <DOMAIN_ID> \
--region <REGION> \
--enable-export
First data available within 24 hours. See: Exporting asset metadata
3. Verify Permissions for Querying
Requires:
- S3 Tables federated catalog registered in Glue (
s3tablescatalog) - Lake Formation SELECT + DESCRIBE grants on the table
Grant access:
aws lakeformation grant-permissions \
--principal DataLakePrincipalIdentifier=<ROLE_ARN> \
--resource '{"Table": {"CatalogId": "<ACCOUNT>:s3tablescatalog/aws-sagemaker-catalog", "DatabaseName": "asset_metadata", "Name": "asset"}}' \
--permissions DESCRIBE SELECT \
--region <REGION>
4. Query
Query syntax:
"s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset"
Constraints:
-
You MUST always filter by
snapshot_time— without it, the query scans all historical snapshots and returns duplicates -
You MUST confirm workgroup and output location before executing
-
Default to
DATE(snapshot_time) = CURRENT_DATEfor current state -
You SHOULD use the key columns documented in this skill to build queries. If you need the full schema, run
get-tablesonce:aws glue get-tables --catalog-id "<ACCOUNT>:s3tablescatalog/aws-sagemaker-catalog" --database-name "asset_metadata" --region <REGION>
Key columns:
| Column | What it holds | Usage |
|---|---|---|
snapshot_time | Partition key — daily snapshot timestamp | Always filter on this |
asset_id | Unique catalog asset identifier | Primary key for lookups |
resource_type_enum | GlueTable, RedshiftTable, S3Collection, etc. | Filter by asset type |
resource_id | ARN or native identifier | Cross-reference with source systems |
asset_name | Business-friendly name | Display, search |
resource_name | Technical name (table name, prefix) | Filtering |
business_description | Business context (NULL if not provided) | Governance gaps |
extended_metadata | map<string,string> — flexible key-value attributes | Use bracket notation: extended_metadata['owningEntityId'] |
asset_created_time | When asset first appeared in catalog | Growth analysis |
asset_updated_time | Last modification time | Freshness checks |
Current catalog state:
SELECT resource_type_enum, COUNT(*) as count
FROM "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset"
WHERE DATE(snapshot_time) = CURRENT_DATE
GROUP BY resource_type_enum
ORDER BY count DESC;
Assets without business descriptions:
SELECT asset_name, resource_name, resource_type_enum, account_id
FROM "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset"
WHERE DATE(snapshot_time) = CURRENT_DATE
AND business_description IS NULL;
Asset growth over last 30 days:
SELECT DATE(snapshot_time) as date, COUNT(*) as total_assets
FROM "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset"
WHERE DATE(snapshot_time) >= CURRENT_DATE - INTERVAL '30' DAY
GROUP BY DATE(snapshot_time)
ORDER BY date DESC;
Time travel — compare current vs 7 days ago (new descriptions added):
SELECT t.asset_id, t.resource_name,
p.business_description as before,
t.business_description as now
FROM "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset" t
JOIN "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset" p
ON t.asset_id = p.asset_id
WHERE DATE(t.snapshot_time) = CURRENT_DATE
AND DATE(p.snapshot_time) = CURRENT_DATE - INTERVAL '7' DAY
AND p.business_description IS NULL
AND t.business_description IS NOT NULL;
Assets by owner:
SELECT extended_metadata['owningEntityId'] as owner, COUNT(*) as count
FROM "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset"
WHERE DATE(snapshot_time) = CURRENT_DATE
AND extended_metadata['owningEntityId'] IS NOT NULL
GROUP BY extended_metadata['owningEntityId']
ORDER BY count DESC;
Filter by metadata form field:
SELECT *
FROM "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset"
WHERE DATE(snapshot_time) = CURRENT_DATE
AND extended_metadata['<metadata-form-name>.<field-name>'] = '<field-value>';
Key Behaviors
- Daily snapshots — exported around midnight per region
- Always filter by
snapshot_time— without it you get all history (duplicates, slow) - One domain per account per region — to switch domains, delete config first
- No additional charge beyond S3 Tables storage + Athena queries
- Read-only — to update asset metadata, use Glue Discovery APIs or SageMaker Unified Studio
Troubleshooting
| Error | Cause | Fix |
|---|---|---|
aws-sagemaker-catalog bucket not found | Export not enabled | Run put-data-export-configuration --enable-export |
Empty results with CURRENT_DATE | First export hasn't run yet (takes up to 24h) | Wait; try yesterday's date |
AccessDenied on query | Missing Lake Formation grants | Grant SELECT + DESCRIBE on the table |
CATALOG_NOT_FOUND | S3 Tables not registered in Glue | Enable integration: S3 console > Table buckets > Enable integration |
| Duplicate rows in results | Missing snapshot_time filter | Add WHERE DATE(snapshot_time) = CURRENT_DATE |
extended_metadata key returns NULL | Key doesn't exist for that asset | Check available keys: SELECT DISTINCT key FROM ... CROSS JOIN UNNEST(map_keys(extended_metadata)) AS t(key) WHERE DATE(snapshot_time) = CURRENT_DATE |
| Cannot update export encryption | Encryption set at creation time only | Delete and recreate export config |
Security Considerations
Data sensitivity: Catalog metadata exposes organizational structure including asset names, ownership, account IDs, naming conventions, and internal resource identifiers. Treat query results as sensitive by default.
Encryption at rest: Always enable KMS encryption when creating the export configuration. Encryption cannot be changed after creation. Additionally, configure SSE-KMS on your Athena workgroup output bucket.
Least-privilege access: Grant Lake Formation SELECT + DESCRIBE only on the specific asset_metadata.asset table to roles that need catalog analytics. Avoid granting access to the entire aws-sagemaker-catalog bucket.
Audit trail: Enable CloudTrail logging for DataZone (PutDataExportConfiguration, GetDataExportConfiguration), Athena (StartQueryExecution, GetQueryResults), and S3 Tables API calls to track who queries catalog metadata.
Credential hygiene: Use IAM roles with temporary credentials for querying. Avoid long-lived access keys for users accessing catalog metadata. Scope down or rotate principals when access is no longer needed.
Additional Resources
Frequently asked questions about Query AWS SageMaker Catalog
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