Unleashing Agentic AI Analytics on Amazon SageMaker with Amazon Athena and Amazon Quick

This article demonstrates how to build an agentic AI analytics platform combining Amazon SageMaker, Athena, and Amazon Quick to enable business users to query complex lakehouse data through natural language interfaces without SQL expertise.

• Integrates S3, AWS Glue, Athena, and Quick for self-service data analytics
• Supports multiple storage formats: CSV external tables, Apache Iceberg, and S3 Tables
• Creates unified SPICE datasets joining customer, order, and line item data
• Configures semantic Topics for natural language Q&A on structured data
• Builds interactive dashboards using Amazon Q's natural language generation
• Establishes Knowledge Bases from unstructured TPC-H specification documents
• Deploys custom Chat Agents combining structured data and contextual knowledge
• Maintains enterprise security and governance through Lake Formation integration

This solution democratizes data access by enabling business users to explore complex lakehouse data conversationally, combining structured analytics with contextual knowledge retrieval while preserving security and governance controls.