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Engineering Data Mesh in Azure Cloud

You're reading from  Engineering Data Mesh in Azure Cloud

Product type Book
Published in Mar 2024
Publisher Packt
ISBN-13 9781805120780
Pages 314 pages
Edition 1st Edition
Languages
Author (1):
Aniruddha Deswandikar Aniruddha Deswandikar
Profile icon Aniruddha Deswandikar

Table of Contents (23) Chapters

Preface 1. Part 1: Rolling Out the Data Mesh in the Azure Cloud
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

Security Architecture for Data Meshes

The big promise of data meshes is to decouple producers and consumers, create data products, and allow the producers and consumers to share and consume the output of a product however they want. In the era of centralized data analytics, access control was governed centrally and was more focused on the consumption of the data stored in a central data repository because the serving and consuming layers were one and the same. In the data mesh world, this concept needs to change. Data is owned by the data product team. They are responsible for managing access to it. Additionally, they know which data is sensitive and which is not. While data product owners decide the parameters for access to their data, some security standards need to be maintained based on the company’s policies. In this chapter, we will look at how security principles apply to distributed data ownership.

In this chapter, we’re going to cover the following main topics...

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