Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Engineering Data Mesh in Azure Cloud

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

Arrow left icon
Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781805120780
Length 314 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Aniruddha Deswandikar Aniruddha Deswandikar
Author Profile Icon Aniruddha Deswandikar
Aniruddha Deswandikar
Arrow right icon
View More author details
Toc

Table of Contents (23) Chapters Close

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 22. Other Books You May Enjoy

Exploring different methods available for sharing data

One of the challenges most companies face is deciding on a data-sharing best practice that standardizes one standard technique to share data. However, while discussing the challenges of data sharing in a data mesh, we realized that there are multiple ways of sharing data. Let us spend some time organizing all these data-sharing techniques and the different components and layers involved in their implementation. This will help in aligning the different methods to different data-sharing scenarios.

In-place access

As discussed in the What is in-place sharing? section, in-place sharing provides direct access to the data from the code that needs to process that data.

As an example, let’s assume a file called data.csv is stored in a data lake of a European finance data product in a company, as shown in Figure 13.5.

Figure 13.5 – data.csv file location

Figure 13.5 – data.csv file location

An executive dashboard team in...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image