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240503 ||| eng |
| 020 |
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|a 9781805128946
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| 050 |
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4 |
|a T58.6
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| 100 |
1 |
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|a Deswandikar, Aniruddha
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| 245 |
0 |
0 |
|a Engineering Data Mesh in Azure Cloud
|h [electronic resource]
|b Implement Data Mesh Using Microsoft Azure's Cloud Adoption Framework
|c Aniruddha Deswandikar
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| 250 |
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|a 1st edition
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| 260 |
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|a Birmingham
|b Packt Publishing, Limited
|c 2024
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| 300 |
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|a 314 p.
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| 505 |
0 |
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|a Landing zone deployment -- Self-service portal -- Customized templates -- Summary -- Chapter 7: Building a Self-Service Portal for Common Data Mesh Operations -- Why do we need a self-service portal? -- Gathering requirements for the self-service portal -- Requesting a data product zone -- Browse and reuse pipeline -- Data discovery -- Access management -- Requesting landing zones or data products -- Data catalog -- Hosting common data pipeline templates -- Azure Data Factory -- Azure Data Factory instance -- Integration runtime -- Creating linked services -- Create a sequence of activities
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| 505 |
0 |
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|a Streamlining deployment through DevOps -- Summary -- Chapter 4: Building the Data Mesh Governance Framework Using Microsoft Azure Services -- Data mesh governance requirements -- Data catalog -- Collecting and managing metadata -- Step 1 -- ensure accuracy and completeness -- Step 2 -- verify data classification -- Step 3 -- add a business glossary -- Step 4 -- add lineage information -- Monitoring and managing data quality -- Implementing data observability -- Summary -- Chapter 5: Security Architecture for Data Meshes -- Understanding the security requirements of data mesh architecture
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| 505 |
0 |
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|a Building the technology stack -- The analytics team -- Data governance -- Approaches to building your data mesh -- Summary -- Chapter 3: Deploying a Data Mesh Using the Azure Cloud-Scale Analytics Framework -- Introduction to Azure CSA -- Understanding landing zones -- Organizing resources -- Designing a cloud management structure -- Hierarchical policies -- Diving deeper into landing zones in CSA -- Data management landing zone -- Data landing zone -- Automating landing zone deployment -- IaC -- Organizing resources in a landing zone -- Networking topologies -- Security and access control
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| 505 |
0 |
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|a Cover -- Title Page -- Copyright -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1: Rolling Out the Data Mesh in the Azure Cloud -- Chapter 1: Introducing Data Meshes -- Exploring the evolution of modern data analytics -- Discovering the challenges of modern-day enterprises -- DaaP -- Data domains -- The data mesh solution -- Summary -- Chapter 2: Building a Data Mesh Strategy -- Is a data mesh for everybody? -- Aligning your analytics strategy with your business strategy -- Understanding data maturity models -- Stage 1 -- Stage 2 -- Stage 3 -- Stage 4
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| 505 |
0 |
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|a Understanding authentication and authorization in Azure -- Managing data access -- SQL Database -- Data lakes -- Data lake structure -- Managing data privacy -- Data masking -- Data retention -- Summary -- Chapter 6: Automating Deployment through Azure Resource Manager and Azure DevOps -- Azure Resource Manager templates for landing zones -- Understanding the ARM template structure -- Source code control for ARM templates -- Azure DevOps pipelines for deploying infrastructure -- Base data product templates -- T-shirt sizing -- Landing zone requests -- Landing zone approval
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| 653 |
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|a Gestion / Informatique
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| 653 |
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|a Big data / http://id.loc.gov/authorities/subjects/sh2012003227
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| 653 |
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|a Management information systems / http://id.loc.gov/authorities/subjects/sh85080359
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| 653 |
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|a Systèmes d'information de gestion
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| 653 |
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|a Cloud computing / http://id.loc.gov/authorities/subjects/sh2008004883
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| 653 |
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|a Business / Data processing / http://id.loc.gov/authorities/subjects/sh85018264
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| 653 |
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|a Microsoft Azure (Plateforme informatique)
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| 653 |
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|a Microsoft Azure (Computing platform) / http://id.loc.gov/authorities/subjects/sh2016001752
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Description based upon print version of record. - Parameterize the pipeline
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9781805120780/?ar
|x Verlag
|3 Volltext
|
| 082 |
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|a 658
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| 082 |
0 |
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|a 330
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| 082 |
0 |
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|a 658.4/038011
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| 520 |
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|a 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
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| 520 |
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|a 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.
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| 520 |
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|a 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 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.
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| 520 |
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|a 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, spending months trying to organize domains. This is where Engineering Data Mesh in Azure Cloud can help.
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