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

Chapter 12: Visualizing Data with Amazon QuickSight

In Chapter 11, Ad Hoc Queries with Amazon Athena, we looked at how Amazon Athena enables data analysts to run ad hoc queries against data in the data lake using the power of SQL. And while SQL is an extremely powerful tool for querying large datasets, often, the quickest way to understand a summary of a dataset is to visualize the data in graphs and dashboards.

In this chapter, we will do a deeper dive into Amazon QuickSight, a Business Intelligence (BI) tool that enables the creation of rich visualizations that summarize data, with the ability to filter and drill down into datasets in numerous ways.

In smaller organizations, a data engineer may be tasked with setting up and configuring a BI tool that data consumers can use. Things may be different in larger organizations, where there may be a dedicated team to manage the BI system. However, it is still important for a data engineer to understand how these systems work, as...