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

Examining examples of real-world data pipelines

The data pipeline examples that we have used in this book have been based on common types of transformations and pipelines, but they have been relatively simple examples. As you can imagine, in large organizations, the types of data pipelines that are built can be a lot more complex and may end up processing extremely large sets of data.

In this section, we will examine two examples of more complex data engineering pipelines from two very well-known organizations – Spotify and Netflix. Both of these companies have public blogs that cover software and data engineering, and the details provided about their pipelines in this section have been taken from the public information that's been made available in a variety of blog posts and articles.

A decade of data wrapped up for Spotify users

Every year, for the past few years, the music streaming service Spotify has used the extensive data they have on their user's...