Unleashing Agentic AI Analytics on Amazon SageMaker with Amazon Athena and Amazon Quick
Amazon AWS AI Blog / 5/1/2026
💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage
Key Points
- The article explains how Amazon Quick’s agentic AI assistant can turn data analytics into a self-service workflow.
- It uses Amazon S3 as a central data storage layer, with Amazon SageMaker and AWS Glue supporting a lakehouse-style setup.
- Amazon Athena is positioned as a serverless SQL engine that can query multiple data formats, including S3 Table, Iceberg, and Parquet.
- Overall, the post focuses on an end-to-end architecture that combines agentic AI with managed AWS analytics components for flexible, queryable data access.
This post demonstrates how agentic AI assistant from Amazon Quick transform data analytics into a self-service capability by using Amazon Simple Storage Service (Amazon S3) as a storage, Amazon SageMaker and AWS Glue for lakehouse, Amazon Athena for serverless SQL querying across multiple storage formats (S3 Table, Iceberg, and Parquet).
💡 Insights using this article
This article is featured in our daily AI news digest — key takeaways and action items at a glance.
Related Articles
Black Hat USA
AI Business
Builder Platforms Fail at Production. Here's What Changed for Us with Nometria
Dev.to
A beginner's guide to the Gemini-2.5-Flash model by Google on Replicate
Dev.to
Hugging Face 'Spaces' now acts as an MCP-App-Store. Anybody thinking on the security consequence?
Dev.to
8 AI Prompts That Win Freelance Clients (Copy-Paste Ready for 2026)
Dev.to
