Category: Technical How-to

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.

This blog post shows how data owners and business intelligence (BI) administrators can centrally manage fine-grained data permissions on Amazon Redshift tables and views and enforce them on all users in Amazon QuickSight with AWS IAM Identity Center trusted identity propagation.

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 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.

Amazon QuickSight is a business intelligence (BI) solution that any organization can leverage to share data and insights to anyone in the organization. As a serverless BI tool, it offers a comprehensive set of advanced analytics features, and one core benefit is that pricing is consumption based. That being said, Quicksight can be leveraged for different use cases and each variant might require a different method to track and report on the cost of running QuickSight. In this post, we explore how you can use AWS CUR and AWS tags to monitor how specific users are using QuickSight. We also discuss how these tags can help organizations implement cloud cost controls by providing the data needed to support custom chargeback reporting.

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 cloud-native, serverless business intelligence (BI) service that lets you build visualizations, perform ad hoc analysis, and gain insights through machine learning (ML) capabilities such as anomaly detection, forecasting, and natural language querying. QuickSight utilizes its robust in-memory engine SPICE (Super-fast, Parallel, In-memory Calculation Engine) to rapidly perform advanced calculations and deliver visuals.BigQuery is Google Cloud’s fully managed, petabyte-scale, cost-effective analytics data warehouse that lets you run analytics over vast amounts of data in near-real time. In this post, we walk you through the permissions and connection details needed in BigQuery to bring BigQuery data into QuickSight through OAuth and create a simple dashboard.