Preface
The adoption of cloud-native business intelligence (BI) tools, such as Amazon QuickSight, enables organizations to gather insights from data at scale. This book is a practical guide to performing simple-to-advanced tasks with Amazon QuickSight.
You'll begin by learning QuickSight's fundamental concepts and how to configure data sources. Next, you'll be introduced to the main analysis-building functionality of QuickSight to develop visuals and dashboards. The book will also demonstrate how to develop and share interactive dashboards with parameters and onscreen controls. Advanced filtering options with URL actions will then be covered, before learning how to set up alerts and scheduled reports. Later, you'll explore the insight visual type in QuickSight using both existing insights and by building custom insights. Further chapters will show you how to add machine learning 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 about advanced topics such as embedded dashboards and multitenancy.
By the end of this book, you'll be well versed in QuickSight's BI and analytics functionalities that will help you create BI apps with ML capabilities.
Who this book is for
This book is for BI developers and data analysts who are looking to create interactive dashboards using data from Lake House on AWS with Amazon QuickSight. This book will also be useful for anyone who wants to learn Amazon QuickSight in depth using practical examples. You will need to be familiar with general data visualization concepts; however, no prior experience with Amazon QuickSight is required.
What this book covers
Chapter 1, Introducing the AWS Analytics Ecosystem, starts by introducing the AWS analytics ecosystem. Then we will discuss how Amazon QuickSight fits within the wider ecosystem. We will look closer at the Lake House architecture and its benefits and different components. Finally, we will provide a step-by-step guide for the reader to set up this architecture in their development environment and add demo data that we will use with Amazon QuickSight to create visualizations later in the book.
Chapter 2, Introduction to Amazon QuickSight, introduces Amazon QuickSight and its main benefits as a cloud-native BI tool. We will explain the various options at the account creation stage, including the user authorization options. Finally, we will provide a step-by-step guide for the reader to set up a QuickSight account and configure the required permissions to connect to Amazon Redshift.
Chapter 3, Preparing Data with Amazon QuickSight, focuses on how to create data sources with Amazon QuickSight and use the dataset editor. We will provide a step-by-step guide to help readers set up data sources on their environment. Finally, we will look at more advanced operations such as joins, row-level security controls, and calculated fields.
Chapter 4, Developing Visuals and Dashboards, introduces the main analysis-building functionality of Amazon QuickSight. We will start by exploring the author UI and explain the different visual types. After adding certain visual types and explaining their functionality we will introduce the concepts of dashboards and stories and explain how we can share these dashboards with other users. Finally, we will look how to style a dashboard and create custom themes.
Chapter 5, Building Interactive Dashboards, explores how to develop interactive dashboards with Amazon QuickSight. The reader will learn to add custom controls on their dashboards and add interactivity to their BI application using parameters. We will also look at advanced filtering options with point-and-click actions with URL actions. Finally, we will explore the reader user experience via the web and mobile QuickSight app and we will explain how to set up alerts and scheduled reports.
Chapter 6, Working with ML Capabilities and Insights, explores the insight visual type in Amazon QuickSight. We will use both the QuickSight-suggested insights and build our own custom insights. We will add forecasting capabilities by analyzing time-series data, and we will add narratives and outlier detection. Finally, we will look more closely at how to integrate Amazon QuickSight with models deployed with Amazon SageMaker.
Chapter 7, Understanding Embedded Analytics, dives deeper into embedded dashboards. We will describe the architecture and the business drivers behind embedding, and we will explain the permission models. We will have a step-by-step guide to set up embedded analytics for authenticated or unauthenticated users. Finally, we will look briefly at how to embed the QuickSight console for QuickSight authors.
Chapter 8, Understanding the QuickSight API, explores patterns to automate certain operations using the QuickSight API. We will see how to create dashboards and reuse analyses using the Template API. We will also explore patterns to automate monitoring of dataset operations and finally, we will look more closely into the API actions that allow us to control settings.
Chapter 9, Managing QuickSight Permissions and Usage, focuses on data permissions and managing Amazon QuickSight operations. We will explain how it integrates with Lake Formation Redshift and Redshift Spectrum tables from a data authorization point of view. We will look at incident reporting using AWS CloudTrail and will examine the use of common operations to manage QuickSight usage.
Chapter 10, Multitenancy in Amazon QuickSight, talks about multitenancy in Amazon QuickSight. To understand it better, we will look at a simple hands-on example. Finally, we will look at an architecture that combines the two concepts of embedded analytics and multitenancy and explain its practical use cases.
To get the most out of this book
You will need to be familiar with general data visualization concepts, but won't need any previous experience with Amazon QuickSight. Also, we expect you to have a basic understanding of the AWS cloud.
If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book's GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.
Please ensure that you terminate all running instances of AWS when not needed, to reduce costs.
Download the example code files
You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Actionable-Insights-with-Amazon-QuickSight. If there's an update to the code, it will be updated in the GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots and diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781801079297_ColorImages.pdf.
Conventions used
There are a number of text conventions used throughout this book.
Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "For example, in QuickSight, the DeleteDataSet action deletes a dataset."
A block of code is set as follows:
$aws quicksight update-user --user-name author-iam --role AUTHOR --custom-permissions-name custom-author --email <your-email> --aws-account-id <account-id> --namespace default --region us-east-1
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
{
"Status": 200,
"EmbedUrl": "https://us-east-1.quicksight.aws.amazon.com/... ?code=...&identityprovider=quicksight&isauthcode=true",
"RequestId": "21d2ad96-3c2b-42a4-ae10-8eb28b20892c"
}
Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "With the Manage Users option selected, click on Manage Permissions as shown."
Tips or important notes
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