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Key benefits

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

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, 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.

Who is this book 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.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

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S.Kundu Apr 15, 2024

Engineering Data Mesh in Azure Cloud helps you to implement Data Mesh approach using different Azure Services. This book provides all information on all challenges along with the solutions involved in implementing data mesh in Microsoft Azure. This book can be helpful to those who want to explore the idea of building a distributed analytical system to handle future growth and agility requirements.A few important topics of the book that I want to highlight are as below:The First part of the book starts with an introduction to Data Mesh architecture and the reason behind considering Data Mesh. It covers the evolution of Data Architecture over time and different challenges of modern day enterprises and how Data Mesh can solve these problems. It also covers different approaches to build your Data Mesh architecture.The book will explain how to deploy a Data Mesh using Azure Cloud Scale Analytics Framework along with building a Data Governance Framework using Microsoft Azure Services.Then it will focus on Security Architecture for Data Mesh and automating the deployment through ARM and Azure DevOps. It will also help you to build a Self-Service Portal for Common Data Mesh Operations.In the next part it will cover the topics of how you can design, build and manage Data Contracts and how to link data contracts to data consumption or pipelines. It will explain you the Data Quality Management related topics along with popular data quality frameworks and tools and go through MDM design patterns and the MDM architecture for a data mesh.It will then cover the Data Mesh and data observability part along with monitoring Data Mesh costs. It will go through different data sharing challenges in Data Mesh along with exploring different methods available for sharing data.The final part covers different popular Data Product Architectures where it will teach you Advanced Analytics using Azure Machine Learning, Databricks and Lakehouse Architecture. It will also teach you how to work on Big Data Analytics using Azure Synapse Analytics along with Event Driven Analytics using Azure Event Hubs, Azure Stream Analytics. It will cover the topics on AI using Azure Cognitive Services and Azure OpenAI.

Amazon Verified review

J. May 13, 2024

This book is compact and full of both conceptual and practical information. The author starts from zero, explaining data mesh, both the what and the why, and then proceeds to show how to implement one using Azure. Very well written. To the point.

Amazon Verified review

Steven Fernandes May 05, 2024

This book serves as a comprehensive guide for professionals looking to implement a data mesh on the Azure Cloud. It thoroughly covers the strategic planning required to build a collaborative analytics platform, guiding readers through the essential steps from inception to execution. Key topics include the design and management of data contracts, overcoming common challenges, and the intricacies of monitoring and governance. The book also delves into the technical aspects of creating a self-service analytics portal and designing a secure data mesh architecture. Moreover, it offers practical solutions for the challenges faced during data mesh adoption, making it an invaluable resource for anyone embarking on or managing such a project. This guide is well-structured, providing clear and actionable insights that are crucial for successfully implementing a data mesh in a cloud environment.

Amazon Verified review

Shanthababu Pandian Jun 24, 2024

Data engineering is a pivotal function in the industry, and the emergence of data mesh has significantly transformed this practice. This book is a perfect resource, offering a comprehensive understanding of Data Mesh concepts with practical insights, empowering you to implement these concepts effectively in Azure Cloud.The book provides a comprehensive introduction to data meshes, guiding us through the process of building a data mesh strategy. It helps us evaluate our organization’s analytics maturity level, align our strategy with the business, and explore the role of data mesh architecture in this context.The author delves into the practical aspects of deploying data mesh using Azure Cloud-scale Analytics and building a data mesh governance framework using Microsoft services. This focus on Azure Cloud makes the book highly relevant and engaging for Azure Cloud professionals.Following this, the security architecture for data mesh is incredible. It covers various aspects of data security, such as access control and retention, and the security challenges associated with distributed data.Chapters on automating Deployment through Azure Resource Manager and Azure DevOps and Building a Self-Service Portal for Common Data Mesh Operations offer more than just theoretical knowledge. They provide practical insights and solutions that can be immediately implemented in real-world scenarios.These topics are highly recommended for professionals seeking to enhance their data engineering skills.The author, a seasoned expert in designing, building, and managing data contracts, brings a wealth of experience to the table. His deep understanding of data engineering and Azure Cloud services is evident in the formal methods and processes he discusses for maintaining data contracts and SLAs.This expertise builds trust and increases the reliability of the data mesh concepts presented in the book.Data Quality and Master Data Management concepts are key parameters in data.The author explores how, as data mesh grows, data products depend on each other for their outcomes. The explanation of how the bad data quality of one data product could impact multiple products is impressive.Monitoring and Data Observability are other vital concepts we must understand in the data world. He covers monitoring and data observability, which are crucial for data mesh as they enable real-time insights into health and performance.The author explored how to monitor data mesh costs and build a cross-charging model.Specific chapters to understanding data-sharing topologies in a Data Mesh are powerful content in this book, along with various data-sharing topologies, and the different scenarios for using each topology are described.He covered famous data product architecture using advanced analytics aspects by bringing Azure Machine Learning, Databricks, Big Data Analytics Using Azure Synapse Analytics, Event-Driven Analytics Using Azure Event Hubs, Azure Stream Analytics, Azure Machine Learning, AI Using Azure Cognitive Services, and Azure OpenAI.The author gives detailed instructions about data mesh in Azure cloud infrastructure, which is an amazing way to help Data Engineers understand and implement it precisely.Overall … I can give 4.5/5.0 for this. Indeed, an extraordinary effort from the author is much appreciated.-Shanthababu PandianAI and Data Architect | Scrum Master | National and International Speaker | Blogger | Author

Amazon Verified review

Advitya Gemawat May 14, 2024

Some areas covered by the book that stood out to me:🔧 Strong intro on evolution of Data architectures and identifying a strategy to architect your Data Mesh🍱 Utilizing Azure for out-of-the-box Deployment, Governance, and Security capabilities🗃 Dense theory on managing intermediate Data contracts, data quality, & observability📈 My favorite: Practical examples to use AML, Azure Databricks, Azure Synapse, along with a dedicated chapter to connect documents & vector databases using Cognitive Services & Azure OpenAI for custom LLM applicationsThe one limitation I found with this book was the lack of mention about Microsoft Fabric, which is supposed to tie all these data products together in a unified experience. Chapters 14-17 on practical applications are quite useful to learn individual services, but the most relevant learning (and quickest onboarding) may come out of services like Fabric to manage the E2E lifecycle of creating, connection, and testing different services. I'm hoping a future version of this book may adapt Part 4 to center around the overarching experiences Fabric would unlock.I can see Data Architects / technical leads needing to create/maintain large silos of analytics projects directly benefit with Part 1 of the book to holistically analyze their data engineering requirements.Data Engineers will get the quickest benefit from getting hands-on with Azure, but the theory highlighted in Part 2 will perhaps be the most helpful for longer-term as they tackle more technically complex projects.

Amazon Verified review

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

What do you get with Print?