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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

Requirements

Let’s look at a few examples of real-time events and data in some industries:

  • A retail company wants to monitor its customers’ behavior on its website in real time. It wants to see what its customers are clicking, browsing, and adding or removing from their baskets. This will help them understand customer behavior so that they can personalize ads and offers.
  • A manufacturing company wants to monitor the machines they sell for usage and health. They place multiple sensors on the machine that transmit data every second. This data is then put through machine learning models to predict failures.
  • A vineyard wants to monitor soil moisture, sunlight intensity, and other such parameters to ensure the right time for harvesting, preventing disease, and producing good quality wines. They place sensors on their vineyards to collect and process this data.
  • A city wants to monitor its traffic and public transportation data in real time to provide better...
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