Category: Amazon Quick Suite

Teams that rely on dashboards for daily decisions often must pinch and zoom to interact with controls originally designed for larger displays. Checking revenue during a morning standup, reviewing pipeline metrics between meetings, or monitoring operations while traveling all require extra effort when the dashboard was built for a desktop screen. Mobile Layout for Amazon […]

In this post, I share how AI serves as an accessibility tool for neurodivergent professionals. The system is built on Amazon Quick on your desktop, an AI-powered desktop and web assistant that compensates for executive function gaps every day.

In this post, we show you how to combine case management with agentic automation capabilities in Quick Automate. We introduce case management and explore the lifecycle of cases in an agentic workflow from case creation through processing to resolution. We cover how to create and manage single or multiple cases, automatically track and update status, handle exceptions, and incorporate Human-in-the-loop (HITL) steps within workflows. We also show the case creator-processor pattern that enables dynamic scaling. Finally, we walk through how to structure case management for enterprise processes, including HITL and case tracking, through a real-life use case.

In this post, we walk through what Dataset Enrichment is, how it differs from legacy Topics, and provide three migration scenarios with step-by-step guidance so you can move your business context into the dataset layer with confidence.

Today, we are excited to announce Multi-Dataset Relationships in Amazon Quick Sight. This new capability lets you define logical relationships between Quick Sight datasets and perform runtime joins at query time. Instead of flattening tables ahead of time, you keep each table as its own Quick Sight dataset and declare how those datasets relate to one another inside a Quick Sight Topic.

In this post, we shift from concepts to patterns. For each schema, you’ll find a table structure, use cases, implementation steps, and sample SQL queries. We also cover workarounds for advanced scenarios that require extra modeling steps, and close with a summary of current limitations.

This post is for data architects, business intelligence (BI) engineers, and analytics engineers building or optimizing Quick Sight Topics for natural-language Chat-based exploration.

In this post, we walk through how multi-dataset Topics work, explain how the chat agent uses defined relationships to generate cross-dataset queries, and demonstrate an end-to-end implementation using a retail analytics scenario in Quick Sight.

In this post, we show how AWS Finance used chat agents and Flows in Amazin Quick to transform two of their most time-consuming workflows.

In this post, you will learn how to build an end-to-end integration between Snowflake semantic views and Amazon Quick. The sample data is user review data for a media company. You start by loading movie review data from Amazon Simple Storage Service (Amazon S3) into Snowflake, define a semantic view in SQL to add business meaning, explore it with natural-language queries through Cortex Analyst, and then generate an Amazon Quick dataset and dashboard. The dataset can be created manually or with a provided automation script. By the end, your BI team or AI team can ask natural-language questions against a governed data layer and trust that every response reflects the same business logic.