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

Summary

In this chapter, we explored a variety of data consumers that you are likely to find in most organizations, including business users, data analysts, and data scientists. We briefly examined their roles and then looked at the types of AWS services that each of them is likely to use to work with data.

In the hands-on section of this chapter, we took on the role of a data analyst, tasked with creating a mailing list for the marketing department. We used data that had been imported from a MySQL database into S3 in a previous chapter, joined two of the tables from that database, and transformed the data in some of the columns. Then, we wrote the newly transformed dataset out to Amazon S3 as a CSV file.

In the next chapter, Loading Data into a Data Mart, we will look at how data from a data lake can be loaded into a data warehouse, such as Amazon Redshift.