Engineering Data Mesh in Azure Cloud - by Aniruddha Deswandikar (Paperback)

About the Book

This book will help you explore data mesh from theory to strategy, where you'll implement this approach based on your analytics framework's maturity.

Book Synopsis

Overcome data mesh adoption challenges using the cloud-scale analytics framework and make your data analytics landscape agile and efficient by using standard architecture patterns for diverse analytical workloads

• Delve into core data mesh concepts and apply them to real-world situations
• Safely reassess and redesign your framework for seamless data mesh integration
• Conquer practical challenges, from domain organization to building data contracts
• Purchase of the print or Kindle book includes a free PDF eBook

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.

• Build a strategy to implement a data mesh in Azure Cloud
• Plan your data mesh journey to build a collaborative analytics platform
• Address challenges in designing, building, and managing data contracts
• Get to grips with monitoring and governing a data mesh
• Understand how to build a self-service portal for analytics
• Design and implement a secure data mesh architecture
• Resolve practical challenges related to data mesh adoption

This book is for chief data officers and data architects of large and medium-size organizations who are struggling to maintain silos of data and analytics projects. Data architects and data engineers looking to understand data mesh and how it can help their organizations democratize data and analytics will also benefit from this book. Prior knowledge of managing centralized analytical systems, as well as experience with building data lakes, data warehouses, data pipelines, data integrations, and transformations is needed to get the most out of this book.

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

(N.B. Please use the Look Inside option to see further chapters)