Amazon SageMaker Catalog now exports asset metadata as queryable dataset

Amazon SageMaker Catalog now exports asset metadata as an Apache Iceberg table through Amazon S3 Tables. This allows data teams to query catalog inventory and answer questions such as, “How many assets were registered last month?”, “Which assets are classified as confidential?”, or “Which assets lack business descriptions?” using standard SQL without building custom ETL infrastructure for reporting.

This capability automatically converts catalog asset metadata into a queryable table accessible from Amazon Athena, SageMaker Unified Studio notebooks, AI agents, and other analytics and BI tools. The exported table includes technical metadata (such as resource_id, resource_type), business metadata (such as asset_name, business_description), ownership details, and timestamps. Data is partitioned by snapshot_date for time travel queries and automatically appears in SageMaker Unified Studio under the aws-sagemaker-catalog bucket.

This capability is available in all AWS Regions where SageMaker Catalog is supported at no additional charge. You pay only for underlying services including S3 Tables storage and Amazon Athena queries. You can control storage costs by setting retention policies on the exported tables to automatically remove records older than your specified period.

To get started, activate dataset export using the AWS CLI, then access the asset table through S3 Tables or SageMaker Unified Studio’s Data tab within 24 hours. Query using Amazon Athena, Studio notebooks, or connect external BI tools through the S3 Tables Iceberg REST Catalog endpoint. For instructions, see the Amazon SageMaker user guide. 

Categories: general:products/amazon-sagemaker,marketing:marchitecture/artificial-intelligence,marketing:marchitecture/analytics

Source: Amazon Web Services

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