Author: Roy Yung

In this post, we delve into two game-changing features in QuickSight Pixel-perfect Reports, prompted report and reader scheduling. These tools are transforming how business users access and receive reports that they’re interested in on their preferred schedule.

Amazon QuickSight is a scalable, serverless, machine learning (ML)-powered business intelligence (BI) solution. As a fully managed service, QuickSight lets you create and publish interactive dashboards that can be accessed from any device and embedded into your applications, portals, and websites. Traditionally, building market basket analysis dashboards requires data engineering pipelines that can take weeks to implement, because these often depend on ETL (extract, transform, and load) jobs, complex SQL operations, and updates on the data pipeline. The nested filter capability in QuickSight simplifies this process with a no-code interface. In this post, we show you how to configure nested filters in a QuickSight dashboard and how they can aid in different business use cases within market basket analysis. We show how nested filters can provide more advanced filtering to help solve common challenges with market basket analysis dashboards in four different use cases.

In QuickSight, data is queried from datasets when visuals load within analyses, dashboards, reports, exports, in responses to questions asked in natural language to Amazon Q, or when threshold alerts are being evaluated. Direct queries are sent to the underlying data source every time a request is made. Using SPICE, a refreshable snapshot of the data is cached in QuickSight, and all queries are fulfilled using the latest snapshot in SPICE, no longer connecting to the underlying data source. In this post, we will explore the benefits and factors to consider when using SPICE and direct query mode. Afterwards, we will also discuss when and how to use which query mode most efficiently in different scenarios.