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


Congratulations on completing this chapter. In this chapter, we learned how to add ML capabilities to our dashboards. We learned how to add forecasting, including building complex scenarios with what-if analysis. We also learned how to configure narratives and customize them using the QuickSight narrative editor. Finally, we learned how to create outlier detections and perform contributor analysis for anomaly detection. With the capabilities we learned in this chapter, your users will be able to get access to rich visuals and narratives in natural language with simple calculations or more sophisticated ML-driven calculations.

At this stage, we have learned about most of the capabilities of QuickSight analysis, and by combining the knowledge you have learned so far in this book you can configure complex dashboards that provide rich insights for your business users. In the next chapter, we will learn how to embed these dashboards into your own custom application.