Book Image

Actionable Insights with Amazon QuickSight

By : Manos Samatas
Book Image

Actionable Insights with Amazon QuickSight

By: Manos Samatas

Overview of this book

Amazon Quicksight is an exciting new visualization that rivals PowerBI and Tableau, bringing several exciting features to the table – but sadly, there aren’t many resources out there that can help you learn the ropes. This book seeks to remedy that with the help of an AWS-certified expert who will help you leverage its full capabilities. After learning QuickSight’s fundamental concepts and how to configure data sources, you’ll be introduced to the main analysis-building functionality of QuickSight to develop visuals and dashboards, and explore how to develop and share interactive dashboards with parameters and on-screen controls. You’ll dive into advanced filtering options with URL actions before learning how to set up alerts and scheduled reports. Next, you’ll familiarize yourself with the types of insights before getting to grips with adding ML insights such as forecasting capabilities, analyzing time series data, adding narratives, and outlier detection to your dashboards. You’ll also explore patterns to automate operations and look closer into the API actions that allow us to control settings. Finally, you’ll learn advanced topics such as embedded dashboards and multitenancy. By the end of this book, you’ll be well-versed with QuickSight’s BI and analytics functionalities that will help you create BI apps with ML capabilities.
Table of Contents (15 chapters)
Section 1: Introduction to Amazon QuickSight and the AWS Analytics Ecosystem
Section 2: Advanced Dashboarding and Insights
Section 3: Advanced Topics and Management

Exploring the architecture and user authentication

In this section, we will focus on the architectural components of embedded analytics. To understand this end-to-end architecture, we will break it down into three layers:

  • Web application layer
  • BI and data layer
  • Authentication and authorization layer

This is better represented with the following diagram:

Figure 7.1 – Generic architecture for embedded analytics

Generally, a client typically accesses a web app or a web portal using their web browser. In many cases, the user will need to authenticate with the web app. The client will present user credentials (typically, a username and a password) to the authentication layer, which sends back an access code/token so that the client can communicate with the web application layer. In embedded analytics, the web application layer will be responsible for getting the embedded visuals from the BI layer, which, in turn, would be responsible...