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AndreKurait
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querying-aws-sagemaker-catalog

Query SageMaker Catalog asset metadata with SQL over Apache Iceberg snapshots.

Updated Jun 18, 2026
Search Tags:#analytics#ownership#catalog#governance#sql#sagemaker#asset-metadata
One-click install
npx skills add https://github.com/AndreKurait/claude-marketplace-test --skill querying-aws-sagemaker-catalog
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: querying-aws-sagemaker-catalog
Source: https://github.com/AndreKurait/claude-marketplace-test/tree/main/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog
Command: npx skills add https://github.com/AndreKurait/claude-marketplace-test --skill querying-aws-sagemaker-catalog

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables SQL-based analysis of SageMaker Catalog asset metadata exported as Apache Iceberg in the AWS-managed table bucket, enabling governance, ownership audits, and historical comparisons without building custom ETL.

Core Features & Use Cases

  • Governance & compliance: identify gaps in asset descriptions, ownership, and metadata consistency.
  • Inventory analytics: counts by resource_type, asset growth over time using snapshot_time.
  • Time travel & history: compare current catalog state with past snapshots using snapshot_time.
  • Ownership & descriptions: surface assets missing owners or descriptions for remediation.

Quick Start

Install and configure the SageMaker catalog data export, then run your first SQL query against the asset_metadata.asset table.

Frequently Asked Questions about querying-aws-sagemaker-catalog

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run SQL analytics on SageMaker Catalog asset metadata?▼

You can run SQL analytics on SageMaker Catalog asset metadata by querying the asset_metadata.asset table, which is stored as Apache Iceberg in AWS S3. This allows you to perform governance audits and track asset growth without custom ETL.

How do I track SageMaker Catalog asset growth over time using SQL?▼

You track SageMaker Catalog asset growth by querying the partitioned snapshot_time field in the asset_metadata.asset table. This time-travel functionality lets you compare current catalog states with past daily snapshots for historical analysis.

What is the best way to find SageMaker Catalog assets missing owners or descriptions?▼

The best way to find SageMaker Catalog assets missing owners or descriptions is to run governance SQL queries against the Apache Iceberg asset_metadata.asset table. This surfaces metadata gaps directly for targeted remediation and compliance checks.

Do I need AWS Glue and Lake Formation permissions to query SageMaker Catalog exports?▼

Yes, you need appropriate AWS Glue and Lake Formation permissions to query SageMaker Catalog exports. You must also enable the SageMaker catalog data export to make the Apache Iceberg metadata available in your AWS-managed S3 table bucket.

Can I query Apache Iceberg tables in S3 for SageMaker Catalog compliance without building ETL?▼

Yes, you can query Apache Iceberg tables in S3 for SageMaker Catalog compliance without building ETL. The Skill directly supports SQL queries over the exported metadata to identify gaps in ownership, descriptions, and metadata consistency.

Why does querying SageMaker Catalog metadata require a partitioned snapshot_time field?▼

Querying SageMaker Catalog metadata requires the partitioned snapshot_time field to enable time-travel comparisons across daily snapshots. This partitioning optimizes SQL query performance when tracking historical asset inventory changes and governance states.

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