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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 Part 1: Rolling Out the Data Mesh in the Azure Cloud
Chapter 1: Introducing Data Meshes Chapter 2: Building a Data Mesh Strategy Chapter 3: Deploying a Data Mesh Using the Azure Cloud-Scale Analytics Framework Chapter 4: Building a Data Mesh Governance Framework Using Microsoft Azure Services Chapter 5: Security Architecture for Data Meshes Chapter 6: Automating Deployment through Azure Resource Manager and Azure DevOps Chapter 7: Building a Self-Service Portal for Common Data Mesh Operations Part 2: Practical Challenges of Implementing a Data Mesh
Chapter 8: How to Design, Build, and Manage Data Contracts Chapter 9: Data Quality Management Chapter 10: Master Data Management Chapter 11: Monitoring and Data Observability Chapter 12: Monitoring Data Mesh Costs and Building a Cross-Charging Model Chapter 13: Understanding Data-Sharing Topologies in a Data Mesh Part 3: Popular Data Product Architectures
Chapter 14: Advanced Analytics Using Azure Machine Learning, Databricks, and the Lakehouse Architecture Chapter 15: Big Data Analytics Using Azure Synapse Analytics Chapter 16: Event-Driven Analytics Using Azure Event Hubs, Azure Stream Analytics, and Azure Machine Learning Chapter 17: AI Using Azure Cognitive Services and Azure OpenAI Index Other Books You May Enjoy

Summary

In this chapter, we saw various elements of a data mesh strategy. We covered cultural, organizational, and technical changes that will need to be adopted to build a solid data strategy. Building a comprehensive, forward-looking data strategy will prove critical to building a collaborative data analytics system using a data mesh architecture. It is very important that a company spends a good amount of time and resources on building this strategy before moving forward with the implementation. Moving from a centralized data analytics structure to a decentralized, collaborative structure is a cultural change. Many companies employ a change management process by seeking help from external consulting companies to help employees adopt the change. Understand your current data maturity and select the appropriate strategy to implement a data mesh.

In the next chapter, we will understand how a data mesh architecture can be deployed using Microsoft Azure. Microsoft has built multiple...

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