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Book Overview & Buying
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Table Of Contents
Data Engineering with AWS - Second Edition
By :
Data Engineering with AWS
By:
Overview of this book
This book, authored by a Senior Data Architect with 25 years of experience, helps you gain expertise in the AWS ecosystem for data engineering. This revised edition updates every chapter to cover the latest AWS services and features, provides a refreshed view on data governance, and introduces a new section on building modern data platforms. You will learn how to implement a data mesh, work with open-table formats such as Apache Iceberg, and apply DataOps practices for automation and observability.
You will begin by exploring core concepts and essential AWS tools used by data engineers, along with modern data management approaches. You will then design and build data pipelines, review raw data sources, transform data, and understand how it is consumed by various stakeholders. The book also covers data governance, populating data marts and warehouses, and how a data lakehouse fits into the architecture. You will explore AWS tools for analysis, SQL queries, visualizations, and learn how AI and machine learning generate insights from data. Later chapters cover transactional data lakes, data meshes, and building a complete AWS data platform.
By the end, you will be able to confidently implement data engineering pipelines on AWS.
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Table of Contents (24 chapters)
Preface
Section 1: AWS Data Engineering Concepts and Trends
An Introduction to Data Engineering
Data Management Architectures for Analytics
The AWS Data Engineer’s Toolkit
Data Governance, Security, and Cataloging
Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations
Architecting Data Engineering Pipelines
Ingesting Batch and Streaming Data
Transforming Data to Optimize for Analytics
Identifying and Enabling Data Consumers
A Deeper Dive into Data Marts and Amazon Redshift
Orchestrating the Data Pipeline
Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning
Ad Hoc Queries with Amazon Athena
Visualizing Data with Amazon QuickSight
Enabling Artificial Intelligence and Machine Learning
Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World
Building Transactional Data Lakes
Implementing a Data Mesh Strategy
Building a Modern Data Platform on AWS
Wrapping Up the First Part of Your Learning Journey
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Index