You're reading from Data Engineering with AWS - Second Edition

Product type Book

Published in Oct 2023

Publisher Packt

ISBN-13 9781804614426

Pages 636 pages

Edition 2nd Edition

Languages

Concepts

Data Engineering

Author (1):

Gareth Eagar

Table of Contents (24) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. An Introduction to Data Engineering

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

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Qr code Description automatically generated

Data governance, security, and related topics are some of the most important topics to cover in a book that is all about data. Having the most efficient data pipelines, the fastest data transformations, and the best data consumption tools is not worth much if the data is not kept secure and governed correctly. Data must also be stored and accessed in a way that complies with local laws, and the data needs to be cataloged so that it is discoverable and useful to the organization.Sadly, it is not uncommon to read about data breaches and poor data handling by organizations, and the consequences of this can include reputational damage to the organization, as well as potentially massive penalties imposed by the government. It is also not uncommon for organizations to find that they have massive quantities of data, but that they are getting very little value from that data since it is siloed, of poor quality, or users...