You're reading from Engineering Data Mesh in Azure Cloud Implement data mesh using Microsoft Azure's Cloud Adoption Framework

Product type Paperback

Published in Mar 2024

Publisher Packt

ISBN-13 9781805120780

Length 314 pages

Edition 1st Edition

Languages

Python

Tools

Azure

Concepts

Data Science

Author (1):

Aniruddha Deswandikar

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Table of Contents (23) Chapters

Preface

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

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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Components

In Figure 14.1, starting from left to right, let’s look at each component and understand their functionality/attributes.

Source data

Source data can be document repositories, social media feeds, images, or voice recordings. Semi-structured data is typically stored in comma-separated files (CSV) or the JSON format. These could be customer profiles or product details.

Azure Data Factory

Azure Data Factory is a cloud-scale extract, transform, and load (ETL) framework. It has ready-made connectors to over a hundred different sources. It can connect to SAP, Oracle, SQL Server, and many more enterprise systems and data stores. It can have connectors for various cloud services that extend beyond Azure to Google Cloud Platform (GCP) and Amazon Web Services (AWS).

The following are some resources that can help you learn more about Azure Data Factory and its connectors: