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

What is a data mesh?

The concept of a data mesh was introduced around 2019 by Zhamak Dehghani, who at the time was a consultant for a company called ThoughtWorks. The data mesh architecture was built around four principles:

Domain-oriented, decentralized data ownership
Data as a product
Self-service data infrastructure as a platform
Federated computational governance

Over time, as with data lakes, the term began to mean different things to different people. Some organizations would claim they had implemented a data mesh because they had enabled data sharing between multiple data lakes, while others would go all in with organizational change, in addition to building technology stacks to support a data mesh.

I believe that it is okay for a term to evolve and change, but that does mean that when someone uses a term such as data mesh, you need to ask them exactly what that means to them. If someone defines a data mesh as the ability to share data...