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 3: The AWS Data Engineer's Toolkit

Back in 2006, Amazon launched Amazon Web Services (AWS) to offer on-demand delivery of IT resources over the internet, essentially creating the cloud computing industry. Ever since then, AWS has been innovating at an incredible pace, continually launching new services and features to offer broad and deep functionality across a wide range of IT services.

Traditionally organizations built their own big data processing systems in their data centers, implementing commercial or open source solutions designed to help them make sense of ever-increasing quantities of data. However, these systems were often complex to install, requiring a team of people to maintain, optimize, and update, and scaling these systems was a challenge, requiring large infrastructure spend and significant delays while waiting for hardware vendors to install new compute and storage systems.

Cloud computing has enabled the removal of many of these challenges, including...