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

Concepts

Data Science

Author (1):

Aniruddha Deswandikar

Table of Contents (23) Chapters

Preface

1. Part 1: Rolling Out the Data Mesh in the Azure Cloud

2. Chapter 1: Introducing Data Meshes

3. Chapter 2: Building a Data Mesh Strategy

4. Chapter 3: Deploying a Data Mesh Using the Azure Cloud-Scale Analytics Framework

5. Chapter 4: Building a Data Mesh Governance Framework Using Microsoft Azure Services

6. Chapter 5: Security Architecture for Data Meshes

7. Chapter 6: Automating Deployment through Azure Resource Manager and Azure DevOps

8. Chapter 7: Building a Self-Service Portal for Common Data Mesh Operations

9. Part 2: Practical Challenges of Implementing a Data Mesh

10. Chapter 8: How to Design, Build, and Manage Data Contracts

11. Chapter 9: Data Quality Management

12. Chapter 10: Master Data Management

13. Chapter 11: Monitoring and Data Observability

14. Chapter 12: Monitoring Data Mesh Costs and Building a Cross-Charging Model

15. Chapter 13: Understanding Data-Sharing Topologies in a Data Mesh

16. Part 3: Popular Data Product Architectures

17. Chapter 14: Advanced Analytics Using Azure Machine Learning, Databricks, and the Lakehouse Architecture

18. Chapter 15: Big Data Analytics Using Azure Synapse Analytics

19. Chapter 16: Event-Driven Analytics Using Azure Event Hubs, Azure Stream Analytics, and Azure Machine Learning

20. Chapter 17: AI Using Azure Cognitive Services and Azure OpenAI

21. Index

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22. Other Books You May Enjoy

Popular data quality frameworks and tools

In this section, we will look at some of the frameworks and products available for DQMSs. But we cannot go deep into the design and implementation of these products as that is out of the scope of this book. But I will provide links that you can use to learn more about them.

If you are using Microsoft Purview and don’t want to wait for the data quality features to be generally available, you can opt to go with one of the Microsoft Partner solutions such as Profisee (www.profisee.com). Profisee offers a master data management and data quality solution that integrates with Microsoft Purview. Microsoft Purview and Profisee is a common combination used by many companies.

If you wish to go for an open source framework, you can consider Great Expectations (https://greatexpectations.io/). Great Expectations is one of the most popular open source extensible frameworks for data quality management, being used by many companies.

Great Expectations...