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

Hands-on – creating data transformations with AWS Glue DataBrew

In Chapter 7, Transforming Data to Optimize for Analytics, we used AWS Glue Studio to create a data transformation job that took in multiple sources to create a new table. In this chapter, we discussed how AWS Glue DataBrew is a popular service for data analysts, so we'll now make use of Glue DataBrew to transform a dataset.

Differences between AWS Glue Studio and AWS Glue DataBrew

Both AWS Glue Studio and AWS Glue DataBrew provide a visual interface for designing transformations, and in many use cases either tool could be used to achieve the same outcome. However, Glue Studio generates Spark code that can be further refined in a code editor and can be run in any compatible environment. Glue DataBrew does not generate code that can be further refined, and Glue DataBrew jobs can only be run within the Glue DataBrew service. Glue Studio has fewer built-in transforms, and the transforms it does include are...