Category: Advanced (300)

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.

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.

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.

You can use the advanced functionalities in Amazon QuickSight to analyze data at different dimensions and get granular, actionable insights from your data. QuickSight also enables you to achieve this without having to worry about the complexity with data preparation. With QuickSight level-aware calculations (LAC), users including business analysts, data scientists, and decision-makers can dynamically […]

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.

Are you looking to deploy and manage your Amazon QuickSight assets using the QuickSight asset deployment APIs (also known as asset bundle APIs)? If so, you have two options for exporting your assets: QUICKSIGHT_JSON or CLOUDFORMATION_JSON format. In this post, we explore the pros and cons of each option to help you make the right choice for your organization’s asset needs. We also provide an overview of the API deployment options and some practical guidance on how to use the AWS CloudFormation format. So if you’re ready to learn more about the QuickSight deployment APIs and discover which export option is best for your needs, then let’s get started!

Amazon QuickSight is a fully managed, cloud-native business intelligence (BI) service that you can use to connect to your data and create interactive dashboards that can be shared with tens of thousands of users. The dashboards can be used within QuickSight or embedded in software as a service (SaaS) apps. Today, we’re launching a feature you can use to dynamically update the theme and filters of embedded dashboards and visuals at runtime through the QuickSight Embedding SDK. Runtime filtering and theming of embedded dashboards and visuals can help you seamlessly integrate your SaaS application with QuickSight embedded dashboards and visuals. You can now use the new methods available in the Embedding SDK

Amazon QuickSight is a scalable, serverless, embeddable, machine learning (ML)-powered business intelligence (BI) service built for the cloud. You can now export content to Excel workbooks by selecting multiple tables and pivot table visuals from any sheet of a dashboard on the QuickSight console via schedules or programmatically via a set of new Snapshot Export APIs. This post outlines these new functionalities and guides you through their implementation.

Amazon QuickSight Pixel-perfect Reports enables the creation and sharing of highly formatted, personalized reports containing business-critical data to hundreds of thousands of end-users without any infrastructure setup or maintenance, up-front licensing, or long-term commitments. In this post, we will cover how to use QuickSight, a serverless and cloud-native BI service, and Amazon Simple Storage Service (Amazon S3), an object storage service, to generate reports with specific parameters and have these reports delivered to users in a custom application.