You're reading from Data Engineering with AWS Acquire the skills to design and build AWS-based data transformation pipelines like a pro

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781804614426

Length 636 pages

Edition 2nd Edition

Tools

AWS

Concepts

Data Engineering

Author (1):

Gareth Eagar

View More author details

Table of Contents (24) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. An Introduction to Data Engineering FREE CHAPTER

3. Data Management Architectures for Analytics

4. The AWS Data Engineer’s Toolkit

5. Data Governance, Security, and Cataloging

6. Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations

7. Architecting Data Engineering Pipelines

8. Ingesting Batch and Streaming Data

9. Transforming Data to Optimize for Analytics

10. Identifying and Enabling Data Consumers

11. A Deeper Dive into Data Marts and Amazon Redshift

12. Orchestrating the Data Pipeline

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

14. Ad Hoc Queries with Amazon Athena

15. Visualizing Data with Amazon QuickSight

16. Enabling Artificial Intelligence and Machine Learning

17. Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World

18. Building Transactional Data Lakes

19. Implementing a Data Mesh Strategy

20. Building a Modern Data Platform on AWS

21. Wrapping Up the First Part of Your Learning Journey

22. Other Books You May Enjoy

23. Index

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, although Glue DataBrew recipes can also be run from a Glue Studio job. Glue Studio has fewer built-in transforms, and the transforms it does...