Book Image

Data Engineering with AWS

By : Gareth Eagar
Book Image

Data Engineering with AWS

By: Gareth Eagar

Overview of this book

Written by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS. As you progress, you’ll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You’ll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data. By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.
Table of Contents (19 chapters)
1
Section 1: AWS Data Engineering Concepts and Trends
6
Section 2: Architecting and Implementing Data Lakes and Data Lake Houses
13
Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

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...