Manos Samatas - Actionable Insights with Amazon QuickSight: Develop stunning data visualizations and machine learning-driven insights with Amazon QuickSight

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Early Access Publication: Actionable Insights with Amazon QuickSight

Early Access Production Reference: B17049

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ISBN: 978-1-80107-929-7

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1. Introducing the AWS Analytics Ecosystem
2. An Introduction to Amazon QuickSight
3. Data Preparation with Amazon QuickSight
4. Developing Visuals and Dashboards
5. Building Interactive Dashboards
6. Working with Insights
7. Embedded Dashboarding
8. Automation using the QuickSight API
9. Data Permissions and Managing Operations
10. Multitenancy with Amazon QuickSight

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 Lake House architecture and we will discuss its benefits and its components. Finally, we will provide a step-by-step guide to set up a Data Lake House architecture on AWS and load and query a demo data sample. Some of this information may already be familiar to you, but lets go back over the basics.

In this chapter, we will cover the following topics:

• Discovering the AWS analytics ecosystem
• Exploring the Data Lake House architecture on AWS
• Creating a basic Lake House architecture

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.

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.

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 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.