You're reading from Data Engineering with AWS Learn how to design and build cloud-based data transformation pipelines using AWS

Product type Paperback

Published in Dec 2021

Publisher Packt

ISBN-13 9781800560413

Length 482 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Data Analysis

Author (1):

Gareth Eagar

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. Chapter 1: An Introduction to Data Engineering FREE CHAPTER

3. Chapter 2: Data Management Architectures for Analytics

4. Chapter 3: The AWS Data Engineer's Toolkit

5. Chapter 4: Data Cataloging, Security, and Governance

6. Section 2: Architecting and Implementing Data Lakes and Data Lake Houses

7. Chapter 5: Architecting Data Engineering Pipelines

8. Chapter 6: Ingesting Batch and Streaming Data

9. Chapter 7: Transforming Data to Optimize for Analytics

10. Chapter 8: Identifying and Enabling Data Consumers

11. Chapter 9: Loading Data into a Data Mart

12. Chapter 10: Orchestrating the Data Pipeline

13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

14. Chapter 11: Ad Hoc Queries with Amazon Athena

15. Chapter 12: Visualizing Data with Amazon QuickSight

16. Chapter 13: Enabling Artificial Intelligence and Machine Learning

17. Chapter 14: Wrapping Up the First Part of Your Learning Journey

18. Other Books You May Enjoy

Summary

In this chapter, you learned more about the Amazon QuickSight service, a BI tool that is used to create and share rich visualizations of data.

We discussed the power of visually representing data, and then explored core Amazon QuickSight concepts. We looked at how various data sources can be used with QuickSight, how data can optionally be imported into the SPICE storage engine, and how you can perform some data preparation tasks using QuickSight.

We then did a deeper dive into the concepts of analyses (where new visuals are authored) and dashboards (published analyses that can be shared with data consumers). As part of this, we also examined some of the common types of visualizations available in QuickSight.

We then looked at some of the advanced features available in QuickSight, including ML Insights (which uses machine learning to detect outliers in data and forecast future data trends), as well as embedded dashboards (which enable you to embed either the full...