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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

Approaches to building your data mesh

Depending on the current stage of the analytics system, you could choose from two broad approaches to building your data mesh – a green-field approach or a surround approach.

If your business is at Stage 1 or 2 of data analytics maturity and you currently don’t have much data but see potential growth coming in the future, use the green-field approach where you start with a clean slate, describe your domains and products, and link them together in a data mesh. You then migrate all existing data from your current analytical system into this new data mesh, distributing the data to the domains and the products it belongs to:

Figure 2.10 – Green-field data mesh implementation

Figure 2.10 – Green-field data mesh implementation

If your business is at Stage 3 or Stage 4 of data maturity, then it will be difficult to start from scratch. Many stakeholders might be dependent on the analytics produced by the central analytics system. In this case, you should...

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