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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 observability

The health of your network defines the health of your infrastructure. A large virtual machine with great specifications to crunch numbers at blazing speed is rendered useless if the network is not working. Similarly, data pipelines are the backbone of a healthy data mesh. Without timely and accurate data, the best analytical system is not useful to the business.

Data quality, data contracts, and master data management are some of the key tenets of the quality and accuracy of data. Along with quality and accuracy, we also need to monitor the movement of data through data lineage in Microsoft Purview as well as data pipeline health through data mesh monitoring.

Throughout multiple chapters in this book, we have looked at all of these aspects of data. As part of data mesh monitoring, you need to bring all of these different data observability metrics together onto a single pane of glass.

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