Amazon Quick introduces Dataset Q&A for conversational analytics against enterprise data

Amazon Quick now supports Dataset Q&A — a conversational analytics capability that enables users to ask natural language questions directly against their enterprise data. Alongside Dashboard Q&A, Dataset Q&A provides a powerful new way to interact with data in Amazon Quick — letting anyone with dataset access explore their data and get meaningful, actionable insights using natural language, while respecting all governance rules including Row Level and Column Level Security policies set by data owners..
Dataset Q&A is powered by Amazon Quick’s text-to-SQL agent, which interprets user questions, identifies the right data, and generates precise SQL — all in a single conversational step. The agent works across various data sources users bring into Amazon Quick — generating engine- and dialect-aware optimized SQL against SPICE or AWS data assets such as Amazon Redshift, Amazon Athena, Aurora PostgreSQL, and Apache Iceberg tables stored in Amazon S3 table buckets. Data owners can enrich their datasets with custom instructions, business definitions, and field descriptions directly in Amazon Quick or through simple file uploads. These curated semantics, together with dataset metadata, are ingested into a knowledge graph that captures the meaning and relationships across data assets, enabling Quick’s orchestrator to accurately identify the most relevant datasets and generate the accurate SQL. The Dataset Q&A agent delivers accurate answers across a broad range of question types — from trend analysis and time-series comparisons to ranking, multi-condition analytical queries, and open-ended exploratory questions. Dataset Q&A also includes an Explain capability, allowing users to step through the reasoning behind each answer, inspect the underlying logic, and validate that the generated SQL correctly interprets their question before acting on the result.
Dataset Q&A is now generally available in all AWS Regions where Amazon Quick is available. To get started, see this blog post.
Quelle: aws.amazon.com