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 7: Transforming Data to Optimize for Analytics

In previous chapters, we covered how to architect a data pipeline and common ways of ingesting data into a data lake. We now turn to the process of transforming raw data in order to optimize the data for analytics and to create value for an organization.

Transforming data to optimize for analytics and to create value for an organization is one of the key tasks for a data engineer, and there are many different types of transformations. Some transformations are common and can be generically applied to a dataset, such as converting raw files to Parquet format and partitioning the dataset. Other transformations use business logic in the transformations and vary based on the contents of the data and the specific business requirements.

In this chapter, we review some of the engines that are available in AWS for performing data transformations and also discuss some of the more common data transformations. However, this book focuses...