Seeking advice on structuring workspaces for centralized data & departmental self-service

[ak-5537]

Hi everyone,

We currently operate an MVP data platform designed around a medallion architecture. All processing runs in a single workspace and is orchestrated through a metadata-driven master pipeline. Operational metadata, monitoring, and watermarks are stored in a Fabric SQL database.

As we prepare the platform for production, I'd really appreciate advice on how best to organize the workspaces. The key requirements are:

1. Centralized Gold layer
   The platform should produce centralized Gold tables that can be reused across departments.

2. Central ownership of semantic models and reports
   The data engineering team (2 people) will develop and own centralized semantic models and reports. Access to reports and models will likely need to be restricted by department, so that different business teams see different content.

3. Department-level reuse and self-service
   Departments should be able to:

   • Reuse centralized semantic models to build their own reports

   • Consume and reuse centrally managed reports

   • Potentially publish and share reports within their own department in the future

Our end users are not highly technical. Today, most of their work is done in Excel, but they are going through training. We want to give them enough freedom to experiment while still keeping the platform governed and manageable. Departments are relatively small, typically 8-10 users.

Currently, I'm considering a Hub-and-Spoke workspace model with three main workspace types: HUB, REPORTING_HUB, and SPOKE (per department).

HUB workspace

• Contains the core data platform (Bronze/Silver/Gold processing).
• Has two main environments: TEST and PROD
• Feature development is done in temporary FEATURE workspaces
• Responsible for producing centralized Gold tables
• Data access to Gold tables would be manages with OneLake security

REPORING_HUB workspace

• Contains:
• A Lakehouse with shortcuts to the centralized Gold data
• Centralized semantic models
• Centrally managed reports
• Also split into TEST and PROD, with development done in temporary FEATURE workspaces
• Uses Org Apps to distribute reports to relevant user groups, with access scoped by department

SPOKE workspaces(per department)

• Each department has:
• A Lakehouse with shortcuts to the centralized Gold data (relevant for the Department)
• A shared DEV workspace for experimentation and report development
• A PROD workspace for published departmental content
• Promotion from DEV to PROD is handled via Fabric deployment pipelines.
• Business users are granted Build permissions on centralized semantic models relevant to their role, allowing them to:
• Create their own reports on top of the centralized models
• Copy and adapt centrally managed reports
• (In the future) publish and share reports within their own department

Questions:

1. Does the proposed Hub / Reporting Hub / Spoke workspace model align with good practices for a production data platform?

2. Am I missing anything important (for example around security, governance, scaling as more department are added, or deployment perspective)?
3. Are there any recommended improvements or adjustments to the workspace structure?

Thank you in advance for your time and feedback!