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Engineering Data Mesh in Azure Cloud

You're reading from   Engineering Data Mesh in Azure Cloud Implement data mesh using Microsoft Azure's Cloud Adoption Framework

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781805120780
Length 314 pages
Edition 1st Edition
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Author (1):
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Aniruddha Deswandikar Aniruddha Deswandikar
Author Profile Icon Aniruddha Deswandikar
Aniruddha Deswandikar
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Table of Contents (23) Chapters Close

Preface 1. Part 1: Rolling Out the Data Mesh in the Azure Cloud FREE CHAPTER
2. Chapter 1: Introducing Data Meshes 3. Chapter 2: Building a Data Mesh Strategy 4. Chapter 3: Deploying a Data Mesh Using the Azure Cloud-Scale Analytics Framework 5. Chapter 4: Building a Data Mesh Governance Framework Using Microsoft Azure Services 6. Chapter 5: Security Architecture for Data Meshes 7. Chapter 6: Automating Deployment through Azure Resource Manager and Azure DevOps 8. Chapter 7: Building a Self-Service Portal for Common Data Mesh Operations 9. Part 2: Practical Challenges of Implementing a Data Mesh
10. Chapter 8: How to Design, Build, and Manage Data Contracts 11. Chapter 9: Data Quality Management 12. Chapter 10: Master Data Management 13. Chapter 11: Monitoring and Data Observability 14. Chapter 12: Monitoring Data Mesh Costs and Building a Cross-Charging Model 15. Chapter 13: Understanding Data-Sharing Topologies in a Data Mesh 16. Part 3: Popular Data Product Architectures
17. Chapter 14: Advanced Analytics Using Azure Machine Learning, Databricks, and the Lakehouse Architecture 18. Chapter 15: Big Data Analytics Using Azure Synapse Analytics 19. Chapter 16: Event-Driven Analytics Using Azure Event Hubs, Azure Stream Analytics, and Azure Machine Learning 20. Chapter 17: AI Using Azure Cognitive Services and Azure OpenAI 21. Index 22. Other Books You May Enjoy

Data domains

One of the concepts defined in https://www.thoughtworks.com/what-we-do/data-and-ai/data-mesh is the concept of data domains. Data domains are defined as a logical grouping of data and teams aligned with certain business domains, such as sales, marketing, or production. While each of these domains may have multiple data products, all the teams and the data used to build these products fall under the same domain. This domain team is responsible for managing and maintaining the data in their domain. This is described as domain ownership.

However, in reality, we have found that adopting a domain as a concept could be challenging for many companies as every company has its own structure. For example, large global companies that run their business in different geological zones have sales, marketing, production, and local finance departments in every location. Each of these departments works independently based on their local market and country requirements. Clubbing all sales...

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