Engineering Data Mesh in Azure Cloud Implement Data Mesh using Microsoft Azure's Well-Architected Framework

[book24h]

Free Download Engineering Data Mesh in Azure Cloud: Implement Data Mesh using Microsoft Azure's Well-Architected Framework by Aniruddha Deswandikar
English | April 9, 2024 | ISBN: 1805120786 | 266 pages | MOBI | 14 Mb
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 FeaturesDelve into core data mesh concepts and apply them to real-world situationsSafely reassess and redesign your framework for seamless data mesh integrationConquer practical challenges, from domain organization to building data contractsPurchase of the print or Kindle book includes a free PDF eBookBook 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 learnBuild a strategy for implementing a data mesh in Azure CloudPlan your data mesh journey to build a collaborative analytics platformAddress challenges in designing, building, and managing data contractsGet to grips with monitoring and governing a data meshUnderstand how to build a self-service portal for analyticsDesign and implement a secure data mesh architectureResolve practical challenges related to data mesh adoptionWho 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 ContentsA brief introduction to the Data Mesh architectureBuilding a Data Mesh Strategy based on the maturity of your existing Analytics FrameworkPreparing the landing zones for the Data Mesh using the Azure Cloud-Scale Analytics FrameworkBuilding the Governance Framework for the Data Mesh using Microsoft Purview and other Azure ServicesSecurity architecture for the Data MeshAutomating Deployment through Azure Resource Manager and Azure DevOpsBuilding 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 ContractsData Quality Management(N.B. Please use the Look Inside option to see further chapters)

Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me

A kódrészlet megtekintéséhez be kell jelentkezned, vagy nincs jogosultságod a tartalom megtekintéséhez.

Links are Interchangeable - Single Extraction