Analyze Amazon S3 annotations at scale with materialized views

This article demonstrates how to use Amazon S3 annotations to attach business context directly to objects, with S3 Metadata automatically capturing annotation data into queryable Iceberg tables that can be accelerated using materialized views.

• S3 annotations allow attaching up to 1,000 named annotations per object (up to 1 MB each) in flexible formats like JSON, XML, and YAML
• S3 Metadata automatically captures annotations into fully managed Iceberg tables queryable via Amazon Athena and other Iceberg-compatible tools
• Materialized views in AWS Glue pre-compute and store query results, reducing query time by up to 93% and data scanned by 99%
• Complete workflow: extract data from fuel receipts using Amazon Bedrock Data Automation, store as annotations, enable annotation metadata table, create materialized views, and query for fleet analytics
• Applicable across industries including financial services, insurance, healthcare, media, and manufacturing for compliance, auditing, and operational workflows

S3 annotations eliminate the need for separate metadata systems by storing rich context directly with objects, enabling AI agents and analytics tools to discover and understand data at scale without ETL pipelines.