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Data Engineering with AWS

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

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781804614426
Length 636 pages
Edition 2nd Edition
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Author (1):
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Gareth Eagar Gareth Eagar
Author Profile Icon Gareth Eagar
Gareth Eagar
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Toc

Table of Contents (24) Chapters Close

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

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...

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