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Engineering Data Mesh in Azure Cloud

You're reading from  Engineering Data Mesh in Azure Cloud

Product type Book
Published in Mar 2024
Publisher Packt
ISBN-13 9781805120780
Pages 314 pages
Edition 1st Edition
Languages
Author (1):
Aniruddha Deswandikar Aniruddha Deswandikar
Profile icon Aniruddha Deswandikar

Table of Contents (23) Chapters

Preface Part 1: Rolling Out the Data Mesh in the Azure Cloud
Chapter 1: Introducing Data Meshes Chapter 2: Building a Data Mesh Strategy Chapter 3: Deploying a Data Mesh Using the Azure Cloud-Scale Analytics Framework Chapter 4: Building a Data Mesh Governance Framework Using Microsoft Azure Services Chapter 5: Security Architecture for Data Meshes Chapter 6: Automating Deployment through Azure Resource Manager and Azure DevOps Chapter 7: Building a Self-Service Portal for Common Data Mesh Operations Part 2: Practical Challenges of Implementing a Data Mesh
Chapter 8: How to Design, Build, and Manage Data Contracts Chapter 9: Data Quality Management Chapter 10: Master Data Management Chapter 11: Monitoring and Data Observability Chapter 12: Monitoring Data Mesh Costs and Building a Cross-Charging Model Chapter 13: Understanding Data-Sharing Topologies in a Data Mesh Part 3: Popular Data Product Architectures
Chapter 14: Advanced Analytics Using Azure Machine Learning, Databricks, and the Lakehouse Architecture Chapter 15: Big Data Analytics Using Azure Synapse Analytics Chapter 16: Event-Driven Analytics Using Azure Event Hubs, Azure Stream Analytics, and Azure Machine Learning Chapter 17: AI Using Azure Cognitive Services and Azure OpenAI Index Other Books You May Enjoy

Data governance

Finally, the most important part of a modern data strategy is governance. Building a governance plan and documenting it can be a daunting task. It needs to be driven at the leadership level. A good place to start is to begin defining your business glossary and your data classification tags. These two alone can cover a huge portion of your data governance needs.

A business glossary is a standardized understanding of business terms that all employees can refer to and ensure that they are all talking in the same language. It helps remove ambiguity. By associating business glossary terms with data components, those consuming the data are able to understand the content. The term average order value (AOV) might mean different things to different people. But if you have a business glossary that defines what AOV means, then everyone can refer to it and have a common understanding.

Data classification is the process of classifying data into different sensitivity labels...

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