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

Looking at the data analytics big picture

This book was never intended as a deep dive into one specific area of data engineering, although there are many other great books and resources out there that do focus on a single area (such as a deep dive on Spark programming, or on how to use Kafka to ingest streaming data).

Because of this broad topic coverage, you have probably already begun to form a good idea of the different aspects of the bigger picture of data analytics. While it is quite common for data engineering roles to focus on just writing data transform jobs, or just managing the infrastructure to ingest and process streaming data, it is helpful to understand how this integrates with data warehouses/data marts, how different data consumers use data, and how ML and AI fit into the bigger data picture, as we have reviewed in this book.

We have also been focusing on the tasks from the perspective of a single data engineer, but in reality, most data engineers will work as...