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Engineering Data Mesh in Azure Cloud

Engineering Data Mesh in Azure Cloud

By : Deswandikar
4.5 (6)
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Engineering Data Mesh in Azure Cloud

Engineering Data Mesh in Azure Cloud

4.5 (6)
By: Deswandikar

Overview of this book

Decentralizing data and centralizing governance are practical, scalable, and modern approaches to data analytics. However, implementing a data mesh can feel like changing the engine of a moving car. Most organizations struggle to start and get caught up in the concept of data domains, spending months trying to organize domains. This is where Engineering Data Mesh in Azure Cloud can help. The book starts by assessing your existing framework before helping you architect a practical design. As you progress, you’ll focus on the Microsoft Cloud Adoption Framework for Azure and the cloud-scale analytics framework, which will help you quickly set up a landing zone for your data mesh in the cloud. The book also resolves common challenges related to the adoption and implementation of a data mesh faced by real customers. It touches on the concepts of data contracts and helps you build practical data contracts that work for your organization. The last part of the book covers some common architecture patterns used for modern analytics frameworks such as artificial intelligence (AI). By the end of this book, you’ll be able to transform existing analytics frameworks into a streamlined data mesh using Microsoft Azure, thereby navigating challenges and implementing advanced architecture patterns for modern analytics workloads.
Table of Contents (23 chapters)
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Free Chapter
1
Part 1: Rolling Out the Data Mesh in the Azure Cloud
9
Part 2: Practical Challenges of Implementing a Data Mesh
16
Part 3: Popular Data Product Architectures
17
Chapter 14: Advanced Analytics Using Azure Machine Learning, Databricks, and the Lakehouse Architecture
19
Chapter 16: Event-Driven Analytics Using Azure Event Hubs, Azure Stream Analytics, and Azure Machine Learning

Summary

In this chapter, we covered all the important aspects of data mesh governance, starting from infrastructure to data. Governance is critical to a data mesh as federated governance is its key feature. Building a good governance framework from the beginning is critical to the success of a data mesh in an enterprise.

Another very important aspect of a data mesh is its security architecture. As the number of landing zones increases and more data products get added, more people start interacting with the data mesh. Security management becomes critical as the data mesh grows.

In the next chapter, we will dive deeper into the security architecture of the data mesh.

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