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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
Concepts
Author (1):
Aniruddha Deswandikar Aniruddha Deswandikar
Profile icon Aniruddha Deswandikar
LinkedIn
Aniruddha Deswandikar holds a Bachelor's degree in Computer Engineering and is a seasoned Solutions Architect with over 30 years of industry experience as a developer, architect and technology strategist. His experience spans from start-ups to dotcoms to large enterprises. He has spent 18 years at Microsoft helping Microsoft customers build their next generation Applications and Data Analytics platforms. His experience across Application, Data and AI has helped him provide holistic guidance to companies large and small. Currently he is helping global enterprises set up their Enterprise-scale Analytical system using the Data Mesh Architecture. He is a Subject Matter Expert on Data Mesh in Microsoft and is currently helping multiple Microsoft Global Customers implement the Data Mesh architecture.
See other products by 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

Architecture

Let’s look at the architecture for implementing the preceding requirements. This architecture is divided into four stages: ingest, storage, processing, and server. It’s depicted in Figure 15.1:

Figure 15.1 – Big data processing using Azure Synapse Analytics

Figure 15.1 – Big data processing using Azure Synapse Analytics

Take a closer look at this architecture; in the next section, we’ll learn about the components that are used and their functionality.

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Authors (1)

Profile icon Aniruddha Deswandikar
LinkedIn
Aniruddha Deswandikar holds a Bachelor's degree in Computer Engineering and is a seasoned Solutions Architect with over 30 years of industry experience as a developer, architect and technology strategist. His experience spans from start-ups to dotcoms to large enterprises. He has spent 18 years at Microsoft helping Microsoft customers build their next generation Applications and Data Analytics platforms. His experience across Application, Data and AI has helped him provide holistic guidance to companies large and small. Currently he is helping global enterprises set up their Enterprise-scale Analytical system using the Data Mesh Architecture. He is a Subject Matter Expert on Data Mesh in Microsoft and is currently helping multiple Microsoft Global Customers implement the Data Mesh architecture.
Read more
See other products by Aniruddha Deswandikar

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