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

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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 – deploying a Redshift Serverless cluster and running Redshift Spectrum queries

In our Redshift hands-on exercise, we’re going to create a new Redshift Serverless cluster and configure Redshift Spectrum so that we can query data in external tables on Amazon S3. We’ll then use both Redshift Spectrum and Amazon Athena to query data in S3.

Uploading our sample data to Amazon S3

For this exercise, we are going to use some data generated with a service called Mockaroo (https://www.mockaroo.com/). This service enables us to generate fake data with a wide variety of field types and is useful for demos and testing.

We will upload this dataset, containing a list of users, to Amazon S3 and then query it using Redshift Spectrum. Note that all data in this file is fake data, generated with the tool mentioned above. Therefore, the names, email addresses, street addresses, phone numbers, etc. in this dataset are not real.

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