Category: Learning Levels

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.

QuickSight users can now use natural language generation to create pixel-perfect reports – a new capability in Amazon Q for QuickSight that simplifies the way users create and distribute visually-rich, highly-formatted reports to their stakeholders. Pixel-perfect reports are crucial because they ensure that every detail of the report, from layout to formatting, is meticulously controlled and aesthetically pleasing. This not only creates a professional data presentation, it also improves comprehension and decision-making among stakeholders.In this post, we cover how to use generative BI capabilities to accelerate pixel-perfect report designing using Amazon Q in the QuickSight console and deliver it to QuickSight users. Pixel-perfect reports can also be delivered to non-QuickSight users in Excel, CSV, or PDF format using Snapshot Export APIs.

Amazon QuickSight is a cloud-powered, serverless, and embeddable business intelligence (BI) service that makes it straightforward to deliver insights to everyone in your organization. As a fully managed service, QuickSight lets you create and publish interactive dashboards that can then be accessed from any device and embedded into your applications, portals, and websites. QuickSight is expanding filter and control functionality. Previously, a filter could be scoped to a single visual, some visuals, all of the visuals for a dataset on a sheet, or all applicable visuals on a sheet. With the launch of cross-sheet filters and controls, authors can now create, delete, and edit filters that apply to multiple sheets. Previously, cross-sheet filters could be created with parameters in over forty clicks. With the expansion in functionality, they can be created in just five clicks, which shows how much this new feature simplifies setting up filters and controls that impact multiple sheets.

Amazon QuickSight is an IAM Identity Center enabled application. Administrators can create a new QuickSight account and use IAM Identity Center for managing QuickSight users and groups. IAM Identity Center can store QuickSight user and group identities within IAM Identity Center itself as an identity store, or they can configure it with a third-party identity provider (IdP) used within their organization. Administrators can use groups in their IdP to assign QuickSight roles (administrator, author, and reader) to users. After they’re successfully authenticated via IdP, users can seamlessly access QuickSight dashboards either from the QuickSight application or as an embedded component within their business applications. Having QuickSight users and groups provisioned in an IdP also helps control authorization to QuickSight resources like dashboards and datasets.In this post, we discuss when you should use which type of instance, the benefits of using IAM Identity Center with QuickSight, how to configure an account instance, and how to use it to access QuickSight.

This post delves into integrating QuickSight with Slack through AWS Chatbot. AWS Chatbot serves as a ChatOps tool within AWS, facilitating communication and commands through chat clients. It supports notifications from AWS services to chat channels, streamlining monitoring and issue resolution processes.

Amazon QuickSight stands at the forefront of AWS business intelligence (BI) and data visualization offerings, enabling organizations to create and share interactive dashboards, perform one-time analyses, and glean actionable insights from their data. In today’s data-centric business environment, the ability to efficiently generate and distribute insightful reports across different segments or regions remains a critical challenge for many business. Addressing this challenge, we delve into the automation of report processing workflows. For our use case, a real estate customer wants to send state-specific weekly real estate reports for each state to their regional agents. In this post, we show you how to use QuickSight, combined with its Snapshot APIs and other AWS services, to automate this process.

Amazon QuickSight supports identity federation through Security Assertion Markup Language 2.0 (SAML 2.0) in both Standard and Enterprise editions. With federation, you can manage users using your enterprise identity provider (IdP) and pass them to QuickSight at login. IdPs include Microsoft Active Directory Federation Services, Ping One Federation Server, Okta, and more. This post provides steps and code samples to overcome these challenges in a scalable way. We demonstrate the solution using Okta, but you could use other IdPs as well. This is a proven solution and has been used and implemented by several QuickSight customers.

All your trusted BI capabilities of Amazon QuickSight now come with powerful new AI features that integrate chat agents, deep research, and automation in one seamless experience with Amazon Quick Suite! Learn more » You can use the advanced functionalities in Amazon QuickSight to analyze data at different dimensions and get granular, actionable insights from […]

This post provides guidance on deploying QuickSight in a multi-tenant environment, and the considerations around data isolation and deploying resources to tenants in a QuickSight application. Multi-tenancy within applications provides a mechanism to segment groups of users from one another. These groups could be users from different companies, different geographic regions, or different lines of business within an enterprise. Users within different tenants can’t see other users, data, and assets, while reducing the complexity of having a different infrastructure for each set of users.

In this post, we show how to use AWS CloudFormation to deploy all the necessary resources to automate the retry of the ingestion of a failed dataset refresh. This can help speed up the time to have the data available to the users by either completing the refresh successfully or providing more information on the cause of the failure to the dataset owner. Additionally, QuickSight assets can be monitored using Amazon CloudWatch metrics. QuickSight developers and administrators can use these metrics to observe and respond to the availability and performance of their QuickSight ecosystem in near-real time.