From data lake to AI-ready analytics: Introducing new data source with S3 Tables in Amazon Quick

2026-05-04 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

Amazon Quick now supports Amazon S3 Tables (Apache Iceberg tables) as a new data source, enabling direct querying and visualization of large-scale, real-time datasets stored in Amazon S3. This integration allows organizations to combine analytics and AI for faster insights and decision-making, eliminating the need for intermediate data warehouses or OLAP systems. The feature offers streamlined architecture, near real-time insights, and scalable performance by treating the data lake as a central source of truth. It supports both Direct Query and SPICE modes, with a focus on Direct Query for near real-time access. The solution is demonstrated through a financial services use case, showing how transaction data streamed via Amazon Kinesis Data Streams and Amazon Data Firehose can be analyzed instantly using Amazon Quick's natural language chat capabilities, such as "My Assistant", for fraud detection and approval rate monitoring.

Key takeaway

For Directors of AI/ML or Data Analysts building modern data architectures, integrating Amazon Quick with S3 Tables simplifies your stack and accelerates insights. You can achieve near real-time analytics directly from your data lake, reducing operational complexity and costs associated with data movement. Consider using Direct Query mode for use cases requiring immediate data access, such as fraud detection, to empower business users with AI-powered, natural language data exploration without specialized ML expertise.

Key insights

Amazon Quick now directly queries Apache Iceberg tables in S3, enabling near real-time, AI-powered analytics on data lakes.

Principles

• Data lakes as central source of truth
• Minimize data movement for real-time insights
• Simplify architecture by removing intermediate layers

Method

Configure Amazon Quick to access S3 Tables, create a data source pointing to an S3 table bucket, build a dataset by joining tables, and then interact with the dataset using natural language chat agents for real-time analysis.

In practice

• Use Direct Query for near real-time data access
• Join multiple Iceberg tables within Quick datasets
• Employ "My Assistant" for natural language data exploration

Topics

• Amazon Quick
• Amazon S3 Tables
• Apache Iceberg
• Data Lakes
• Near Real-time Analytics

Best for: Director of AI/ML, Data Analyst, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.