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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Streamlining deployment through DevOps

Creating individual landing zones, networking them together, applying policies and security, and repeating this process for every landing zone can be a tedious and time-consuming process. Once the data mesh has been deployed, maintaining it by applying changes and fixes and tracking those changes can add another level of complexity.

DevOps is a set of tools and processes that help automate IT operations. If implemented early, they can help streamline the deployment and maintenance of a data mesh.

Let’s define some key components of DevOps and understand how they are used to automate the data mesh deployment and maintenance:

Repo: A repo (or repository) is a version control system that stores and tracks the artifacts of a process. These include ARM templates, JSON files, and code scripts. Azure supports two types of repos – Git repos and Azure DevOps repos (Team Foundation Version Control (TFVC)). Git is the default...