Engineering Data Mesh in Azure Cloud: Implement data mesh using Microsoft Azure's Cloud Adoption Framework

by Aniruddha Deswandikar

Overview

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.

Product Details

ISBN-13:	9781805128946
Publisher:	Packt Publishing
Publication date:	03/29/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	314
File size:	12 MB
Note:	This product may take a few minutes to download.

About the Author

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.

Table of Contents

Table of Contents

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