Actionable Insights with Amazon QuickSight

Manos Samatas

• Editorial: Packt Publishing
• Año: 2022
• ISBN:
  9781801072007
• Edición: 1
• Categoría: Negocios y empresa
• Categoría: Inteligencia empresarial

Build interactive dashboards and storytelling reports at scale with the cloud-native BI tool that integrates embedded analytics and ML-powered insights effortlesslyKey Features• Explore Amazon QuickSight, manage data sources, and build and share dashboards• Learn best practices from an AWS certified big data solutions architect • Manage and monitor dashboards using the QuickSight API and other AWS services such as Amazon CloudTrailBook DescriptionAmazon 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.What you will learn• Understand the wider AWS analytics ecosystem and how QuickSight fits within it• Set up and configure data sources with Amazon QuickSight• Include custom controls and add interactivity to your BI application using parameters• Add ML insights such as forecasting, anomaly detection, and narratives• Explore patterns to automate operations using QuickSight APIs• Create interactive dashboards and storytelling with Amazon QuickSight• Design an embedded multi-tenant analytics architecture• Focus on data permissions and how to manage Amazon QuickSight operationsWho this book is forThis book is for business intelligence (BI) developers and data analysts who are looking to create interactive dashboards using data from Lake House on AWS with Amazon QuickSight. It will also be useful for anyone who wants to learn Amazon QuickSight in depth using practical, up-to-date examples. You will need to be familiar with general data visualization concepts before you get started with this book, however, no prior experience with Amazon QuickSight is required.

Section 1: Introduction to Amazon QuickSight and the AWS Analytics Ecosystem

This section is an introduction to Amazon QuickSight and Lake House architecture. After completing this part, the reader will understand how to set up Amazon QuickSight, manage data sources, and build and share basic dashboards.

This section consists of the following chapters:

• Chapter 1, Introducing the AWS Analytics Ecosystem
• Chapter 2, Introduction to Amazon QuickSight
• Chapter 3, Preparing Data with Amazon QuickSight
• Chapter 4, Developing Visuals and Dashboards

Chapter 1: Introducing the AWS Analytics Ecosystem

As data increases in both volume and variety, organizations from all verticals are adopting cloud analytics services for their data analytics. AWS offers a number of analytics services covering data lakes, data warehousing, big data processing, extract, transform, load (ETL), and data visualization. In this chapter, we will introduce the AWS analytics ecosystem. Some of the services we discuss here will be mentioned again later in the book.

First, we will map the AWS services into categories. Then, we will discuss how Amazon QuickSight fits into the wider AWS analytics ecosystem. We will look more closely at a modern modern data architecture and we will discuss its benefits and its components. Finally, we will provide a step-by-step guide to set up a modern data architecture on AWS and load and query a demo data sample. Some of this information may already be familiar to you, but let's go back over the basics.

In this chapter, we will cover the following topics:

• Discovering the AWS analytics ecosystem
• Exploring the modern data architecture on AWS
• Creating a basic modern data architecture

Technical requirements

To follow along with this chapter, you will need the following pre-requisites:

• An AWS account with console access
• AWS CLI access

The code sample for this chapter can be accessed on the GitHub repository for this book at https://github.com/PacktPublishing/Actionable-Insights-with-Amazon-QuickSight/tree/main/chapter_1.

Discovering the AWS analytics ecosystem

AWS provides a large number of analytics services. In addition to that, AWS has a number of partners who specialize in data analytics and offer analytics solutions that run on the AWS infrastructure. Partner solutions are not in the scope of this section, however. This section focuses on the AWS fully managed analytics services. In order to list the services, we will first define the specific categories related to analytics functions. Machine learning and predictive analytics are also out of the scope of this chapter. For every service category, we will then list the AWS services available, and for each service, we will provide a high-level description. Figure 1.1 depicts the commonly used AWS analytics services.

Figure 1.1 – AWS analytics services

Business intelligence

More and more organizations aspire to be data-driven and use data to drive their strategic decisions. Business intelligence (BI) tools help organizations to transform data into actionable insights. With the use of BI tools, users can analyze data and then present their findings in reports or dashboards. These reports or dashboards can then be consumed by business users who are interested in getting a picture of the state of the business.

In 2015, AWS launched Amazon QuickSight, a cloud-native BI tool. Since then, AWS has added new features to QuickSight, enriching the standard dashboard functionality with machine learning capabilities and offering embedded dashboard functionality. Amazon QuickSight is the main technology we will be covering in this book. Over the next few chapters, we will start with the basic functionality of Amazon QuickSight, and then we will explore more advanced features. Where possible, we will use practical examples that can be repeated in your own development environment, to give you hands-on experience with Amazon QuickSight.

Data warehousing

Data warehouses are repositories of data; they are important components of the BI process. Data stored in data warehouses is typically structured. Traditionally, data is ingested and centralized into data warehouses from different operational data stores. Data warehouses are optimized to run analytical queries over large amounts of data. The results of analytical queries are usually calculated after an aggregation over multiple rows from one or more tables. BI applications use analytical queries to aggregate data and visualize it. It is a common architectural approach to use a data warehouse to serve data to a BI application.

Back in 2012, AWS launched Amazon Redshift, a cloud-native, fully managed data warehouse service. Today, Redshift is one of the most popular cloud data warehouses with thousands of organizations from different verticals using it to analyze their data. Other popular cloud data warehouses include Snowflake and Google BigQuery. Amazon Redshift integrates with most BI tools and it integrates natively with Amazon QuickSight. We will discuss this topic in more detail in Chapter 3, Preparing Data with Amazon QuickSight, when we look more closely into Amazon QuickSight-supported data sources.

Data lake storage and governance

A data lake is a repository of data where organizations can easily centralize all of their data and apply it in different use cases such as reporting, visualization, big data analytics, and predictive analytics. Data stored in data lakes can be structured or semi-structured. Usually, data is ingested into the data lake in its raw format, and is then transformed and stored back into the data lake for further processing and analysis. A cloud data lake typically uses a cloud object store to store data. AWS introduced Amazon Simple Storage Service (S3) in March 2006, offering developers a highly scalable, reliable, and low-latency data storage infrastructure at very low cost. Amazon S3 can store an unlimited amount of data, a particularly useful feature for data lakes. Organizations have one less thing to w...

Índice

• Actionable Insights with Amazon QuickSight
• Contributors
• Preface
• Section 1: Introduction to Amazon QuickSight and the AWS Analytics Ecosystem
• Chapter 1: Introducing the AWS Analytics Ecosystem
• Chapter 2: Introduction to Amazon QuickSight
• Chapter 3: Preparing Data with Amazon QuickSight
• Chapter 4: Developing Visuals and Dashboards
• Section 2: Advanced Dashboarding and Insights
• Chapter 5: Building Interactive Dashboards
• Chapter 6: Working with ML Capabilities and Insights
• Chapter 7: Understanding Embedded Analytics
• Section 3: Advanced Topics and Management
• Chapter 8: Understanding the QuickSight API
• Chapter 9: Managing QuickSight Permissions and Usage
• Chapter 10: Multitenancy in Amazon QuickSight
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