What is Data Mesh

How It Works

Data mesh decentralizes data ownership by treating data as a product owned by domain teams rather than a centralized data team. Each domain team owns, produces, and maintains their data products with standardized quality and discoverability guarantees. A self-serve data platform provides common infrastructure, and federated governance ensures interoperability across domains.

Technical Details

The four principles are: domain-oriented ownership, data as a product, self-serve data platform, and federated computational governance. Data products expose standard interfaces (APIs, documented schemas, SLAs). The platform provides shared infrastructure for storage, processing, catalog, and access control. Implementation varies from lightweight API standards to full platform engineering efforts with dedicated teams.

Best Practices

• Start with clear domain boundaries aligned to business capabilities, not technology
• Define data product standards including schema documentation, quality SLAs, and access patterns
• Build a self-serve platform that reduces the friction for teams to publish data products
• Implement federated governance that balances domain autonomy with organizational standards

Common Pitfalls

• Treating data mesh as purely a technology solution without organizational change
• Not investing in the self-serve platform, leaving domain teams to build infrastructure from scratch
• Creating data silos by decentralizing without interoperability standards
• Applying data mesh to small organizations where centralized data management works well

Advanced Tips

• Apply data mesh principles to multimodal AI by having domain teams own their modality-specific data products
• Use standardized embedding formats as the interoperability layer between domain data products
• Implement cross-domain data discovery through a federated catalog of multimodal data products
• Build domain-specific data quality metrics that align with each team's multimodal processing requirements