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
Key Features
- 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
Book Description
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 and spend 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 Azure Cloud Adoption Framework 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 implementation and adoption of 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 section covers some common architecture patterns used for modern analytics frameworks like artificial intelligence (AI).
By the end of this book, you’ll be able to transform existing analytics frameworks into a streamlined data mesh using MS Azure, thereby navigating challenges and implementing advanced architecture patterns for modern analytics workloads.
What you will learn
- Build a strategy for implementing 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
Who this book is for
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.
Table of Contents
- A brief introduction to the Data Mesh architecture
- Building a Data Mesh Strategy based on the maturity of your existing Analytics Framework
- Preparing the landing zones for the Data Mesh using the Azure Cloud-Scale Analytics Framework
- Building the Governance Framework for the Data Mesh using Microsoft Purview and other Azure Services
- Security architecture for the Data Mesh
- Automating Deployment through Azure Resource Manager and Azure DevOps
- Building a Self-Service Portal for requesting landing zones, access to datasets and other common Analytics activities and artefacts.
- How to design, build and manage Data Contracts
- Data Quality Management
(N.B. Please use the Look Inside option to see further chapters)
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.
