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 DescriptionDecentralizing 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.What you will learn
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
Who this book is forThis 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.
Sprache
Verlagsort
Maße
Höhe: 235 mm
Breite: 191 mm
Dicke: 18 mm
Gewicht
ISBN-13
978-1-80512-078-0 (9781805120780)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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
Introducing Data Meshes
Building a Data Mesh Strategy
Deploying a Data Mesh Using the Azure Cloud-Scale Analytics Framework
Building a Data Mesh Governance Framework Using Microsoft Azure Services
Security Architecture for Data Meshes
Automating Deployment through Azure Resource Manager and Azure DevOps
Building a Self-Service Portal for Common Data Mesh Operations
How to Design, Build, and Manage Data Contracts
Data Quality Management
Master Data Management
Monitoring and Data Observability
Monitoring Data Mesh Costs and Building a Cross-Charging Model
Understanding Data-Sharing Topologies in a Data Mesh
Advanced Analytics Using Azure Machine Learning, Databricks, and the Lakehouse Architecture
Big Data Analytics Using Azure Synapse Analytics
Event-Driven Analytics Using Azure Event Hubs, Azure Stream Analytics, and Azure Machine Learning
AI Using Azure Cognitive Services and Azure OpenAI
