Listen free for 30 days
-
Data Mesh
- What Is Data Mesh? Principles of Data Mesh Architecture
- Narrated by: Jack Nolan
- Length: 2 hrs and 42 mins
Add to basket failed.
Please try again later
Add to wishlist failed.
Please try again later
Remove from wishlist failed.
Please try again later
Adding to library failed
Please try again
Follow podcast failed
Unfollow podcast failed
One credit a month, good for any title to download and keep.
Unlimited listening to the Plus Catalogue - thousands of select Audible Originals, podcasts and audiobooks.
No commitment - cancel anytime.
£7.99/month after 30 days. Renews automatically. See here for eligibility.
Buy Now for £6.39
No valid payment method on file.
We are sorry. We are not allowed to sell this product with the selected payment method
Pay using card ending in
By completing your purchase, you agree to Audible's Conditions of Use and authorise Audible to charge your designated card or any other card on file. Please see our Privacy Notice, Cookies Notice and Interest-based Ads Notice.
Summary
Gain a deep understanding of data mesh.
Have you ever wondered what data mesh is?
Are you interested in knowing how data mesh is used in our daily lives?
Have you ever wondered how data mesh can change and benefit our daily life?
What Is Data Mesh? Principles Of Data Mesh Architecture is a comprehensive guide to data mesh architecture, a decentralized approach to data governance and management. The audiobook explains the key principles of data mesh and how it differs from traditional approaches to data management.
In this audiobook, you will:
- Discover and take a deep dive into the world of data mesh architecture and how they’re used in everyday life.
- Understand the basics of the domain ownership principle.
- Discover the self-serve data platform principle.
- Learn about data as a product principle.
- Master the federated computational governance principle.
- Understand domain-oriented analytical data interfaces.
- Learn about data products as an architecture quantum.
- Understand the steps leading to designing the data product architecture.
©2023 Brian Murray (P)2023 Brian Murray