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

View More author details

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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Understanding data maturity models

Data analytics maturity models help assess how an organization is leveraging and can leverage data to help the business make decisions. Studies have shown that organizations broadly fall into the following four stages of maturity when it comes to data analytics:

Figure 2.3 – Stages of analytics maturity

Let us learn more about these stages in the following sub-sections.

Stage 1

In this stage, companies ingest data from source systems and transform and move it to a staging area. From the staging area, it is modeled into data marts and served as OLAP cubes. Reporting applications read these cubes and present the data. This stage only caters to structured tabular data available in transactional and legacy sources. The pipelines are all centralized and managed. The system can quickly adapt to new sources as long as those sources provide tabular structured data: