You're reading from Data Engineering with AWS Learn how to design and build cloud-based data transformation pipelines using AWS

Product type Paperback

Published in Dec 2021

Publisher Packt

ISBN-13 9781800560413

Length 482 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Data Analysis

Author (1):

Gareth Eagar

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. Chapter 1: An Introduction to Data Engineering FREE CHAPTER

3. Chapter 2: Data Management Architectures for Analytics

4. Chapter 3: The AWS Data Engineer's Toolkit

5. Chapter 4: Data Cataloging, Security, and Governance

6. Section 2: Architecting and Implementing Data Lakes and Data Lake Houses

7. Chapter 5: Architecting Data Engineering Pipelines

8. Chapter 6: Ingesting Batch and Streaming Data

9. Chapter 7: Transforming Data to Optimize for Analytics

10. Chapter 8: Identifying and Enabling Data Consumers

11. Chapter 9: Loading Data into a Data Mart

12. Chapter 10: Orchestrating the Data Pipeline

13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

14. Chapter 11: Ad Hoc Queries with Amazon Athena

15. Chapter 12: Visualizing Data with Amazon QuickSight

16. Chapter 13: Enabling Artificial Intelligence and Machine Learning

17. Chapter 14: Wrapping Up the First Part of Your Learning Journey

18. Other Books You May Enjoy

Chapter 2: Data Management Architectures for Analytics

In Chapter 1, An Introduction to Data Engineering, we looked at the challenges introduced by ever-growing datasets, and how the cloud can help solve these analytical challenges. However, there are many different cloud services, open source frameworks, and architectures that can be used in analytical projects, depending on business requirements and objectives.

In this chapter, we will discuss how analytical technologies have evolved and introduce the key technologies and concepts that are foundational for building modern analytical architectures, irrespective of whether you build them on AWS or elsewhere.

If you have experience as a data engineer and have worked with enterprise data warehouses before, you may want to skim through the sections of this chapter, and then do the hands-on exercise at the end of this chapter. However, if you are new to data engineering and do not have experience with big data analytics, then the...