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 14: Wrapping Up the First Part of Your Learning Journey

In this book, we have explored many different aspects of the data engineering role by learning more about common architecture patterns, understanding how to approach designing a data engineering pipeline, and getting hands-on with many different AWS services commonly used by data engineers (for data ingestion, data transformation, and orchestrating pipelines).

We examined some of the important issues surrounding data security and governance and discussed the importance of a data catalog to avoid a data lake turning into a data swamp. We also reviewed data marts and data warehouses and introduced the concept of a data lake house.

We learned about data consumers – the end users of the product that's produced by data engineering pipelines – and looked into some of the tools that they use to consume data (including Amazon Athena for ad hoc SQL queries and Amazon QuickSight for data visualization...