What are the key differences between data mesh and traditional data architecture?

What are the key differences between data mesh and traditional data architecture?

Data mesh decentralizes data ownership, promoting cross-functional teams, while traditional architecture centralizes data management.

Can you explain how data mesh improves scalability?

Data mesh allows teams to scale independently by managing their own data domains, reducing bottlenecks in centralized systems.

What are some challenges of implementing a data mesh?

Challenges include cultural shifts, the need for strong governance, and ensuring interoperability between decentralized data domains.

How does data governance work in a data mesh?

In a data mesh, governance is distributed, with each team responsible for their data quality and compliance, guided by overarching policies.

Which approach is better for large organizations?

It depends on the organization's structure and needs; data mesh suits agile, large teams, while traditional architecture may fit more centralized environments.