Category: Learning Levels

In this post, BMW Group demonstrates how they evolved their Cloud Data Hub beyond breaking down data silos to tackle knowledge silos by making Amazon Quick Sight dashboards discoverable and shareable as data products within their centralized platform. The initiative resulted in a remarkable 197.9% growth in Quick Sight adoption among CDH users within just one year, showcasing how transforming accessible data into actionable visual intelligence can drive decisions across all organizational levels.

In this post, we demonstrate how to use Amazon Q in QuickSight to obtain immediate, data-driven insights during business meetings by asking questions in natural language and receiving real-time, multi-visual answers. The solution enables teams to explore data conversationally through the built-in Product Sales topic, making analytics more accessible and actionable without requiring technical expertise or dashboard creation.

In this post, we explore how to implement authentication and authorization requirements for Amazon QuickSight embedded web applications, focusing on various approaches for business intelligence delivery. We specifically address the use case of an enterprise with a central access management process, showing how QuickSight can be securely integrated into internal-facing applications.

In this post, we present a secure solution for delivering Amazon S3 backed content to QuickSight dashboards using public URLs, eliminating the vulnerabilities inherent in public S3 buckets. Specifically, we demonstrate how to use Amazon CloudFront with your private S3 buckets to deliver content to QuickSight. This approach allows organizations to maintain strict security controls while creating visually compelling dashboards that align with their design guidelines and effectively communicate data-driven insights.

In this post, we demonstrate the new console and dashboard embedding features available in Amazon QuickSight, using Amazon Q.

In this post, we demonstrate how to use Amazon Bedrock model invocation logs to enhance the observability of model usage by using Amazon Athena for efficient log querying and Amazon QuickSight for insightful visualizations.

Many organizations use OneLogin as their identity provider (IdP) to control and manage user authentication and authorization centrally. Amazon QuickSight can integrate with OneLogin through the use of single sign-on (SSO) and SAML 2.0 authentication. With this integration, users can access QuickSight using their existing OneLogin credentials, providing a seamless and secure authentication experience. In this post, we walk you through the steps to configure federated SSO to QuickSight with OneLogin as your IdP.

In this post, we are thrilled to feature David Wong, one of our top Amazon QuickSight Community Experts for 2024.

In this post, we explore how to create a simple serverless architecture using AWS Lambda, Amazon Athena, and QuickSight to establish column level lineage. Tracking column-level lineage provides a clear view of each column’s path through different parts of QuickSight, helping to optimize data processing, improve query performance, ensure accuracy, and meet regulatory requirements.

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.