SSSamarth Sinha

Note

Domain Data Mesh Migration

Move from a central data platform to domain-owned data products without losing governance: tag, migrate in waves, keep policies shared.

Oct 2026 · 1 min read

  • BigQuery
  • GCP projects
  • Data catalog
  • IAM
From a central platform to domain-owned projects under shared policies Central platform to Domain tagging. Domain tagging to Domain project A. Domain tagging to Domain project B. Domain tagging to Domain project N. Domain project A to Quality rating. Domain project B to Consumers. Domain project N to Access audits. Shared policies to Domain project N. Central platform one team owns everything Domain tagging owner per dataset Domain project A standard datasets Domain project B standard datasets Domain project N one per domain Quality rating score per source, alerts Consumers discover via catalog Access audits on a fixed schedule Shared policies replication, quality catalog, access
IllustrativeFrom a central platform to domain-owned projects under shared policiesScroll →

Context

A central platform team that owns every dataset becomes the queue for every change, and quality and cost belong to nobody in the business.

The problem

Decentralising ownership without shared rules produces silos: inconsistent layouts, untracked copies, ad-hoc access and costs nobody can attribute.

How it works

  1. Tag every dataset with its business domain and an accountable owner.
  2. Create domain-owned projects with a standard dataset layout, so every domain looks the same from outside.
  3. Migrate in waves, keeping policy central: table replication, data-quality rules, catalog registration and access requests.
  4. Publish a data-access matrix and audit it on a fixed schedule.
  5. Rate each source on a multi-parameter quality score and alert owners on quality and cost by team and service.

Trade-offs

  • Domains duplicate some effort; standard layouts and templates keep it small.
  • It needs a strong platform team: decentralised ownership runs on central tooling.
  • Policies drift unless audits are scheduled and enforced.

When to use it

Use it when ownership, not technology, is the bottleneck. A small organisation with one data team rarely needs it.