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

Understanding data sources

Over the past decade, the amount and the variety of data that gets generated each year has significantly increased. Today, industry analysts talk about the volume of global data generated in a year in terms of zettabytes (ZB), a unit of measurement equal to a billion terabytes (TB). By some estimates, a little over 1 ZB of data existed in the world in 2012, and yet by the end of 2020, there would have been an estimated 59 ZB of data consumed globally.

In our pipeline whiteboarding session (covered in Chapter 5, Architecting Data Engineering Pipelines) we identified several data sources that we wanted to ingest and transform to best enable our data consumers. For each of these data sources that is identified in a whiteboarding session, you need to develop an understanding of the variety, volume, velocity, veracity, and value of data.