Power BI Metadata – Looker Studio Community Connector

Turn your Power BI semantic model into self-service metadata insights in Looker Studio.

The Power BI Metadata connector lets you explore DAX measures, their dependencies, and column usage across your Power BI workspaces – directly inside Looker Studio. It surfaces the structure behind your models so you can answer questions like:

• Which measures depend on this column?
• Where is this measure used across tables and datasets?
• How complex are my DAX expressions?

Designed for Power BI admins, BI engineers, and analysts who want a metadata-level overview without exposing row-level data.

Why Power BI Metadata?

As your Power BI environment grows, it becomes harder to track:

• What measures exist and where.
• How DAX expressions reference tables and columns.
• Dependencies between measures and underlying data.

Traditional usage logs focus on report views and queries – but not on the semantic model itself. This connector focuses on metadata only, giving you a clean, model-centric view you can analyze and share.

Key benefits:

• Visibility – See all measures across selected workspaces in one place.
• Impact analysis – Understand which measures depend on which columns and tables.
• Governance – Support naming conventions, documentation efforts, and modeling standards.
• Safe by design – Works only with metadata from the Power BI Admin API; no row-level fact data is retrieved.

What the connector provides

The connector exposes two logical tables you can analyze in Looker Studio:

1. Measures table

One row per Power BI DAX measure, including:

• Measure identity & location
• measure_id – stable hash ID for joins.
• workspace – workspace name.
• dataset – dataset name.
• table – table name.

• measure_name – measure name.

• DAX & documentation

• expression – full DAX expression.

• description – measure description (if defined in Power BI).

• Dependency metadata

• associated_columns – comma-separated Table[Column] references.
• associated_measures – comma-separated measure references ([Measure]).
• associated_tables – distinct tables referenced by the measure.

This table is ideal for:

• Cataloging measures per workspace/dataset.
• Building documentation dashboards.
• Identifying undocumented or complex measures.

2. Measure–Column Lineage table (Measure–Column Associations)

One row per resolved (measure, underlying column) usage, including cases where the column is only referenced indirectly via other measures.

Core fields include:

• workspace_id, dataset_id – IDs for stable joins and filtering.
• measure_id, measure_name – identity of the root measure.
• table_name, column_name – underlying table/column being used.
• association_degree – how many hops away the column is (1 = direct, 2+ = via intermediate measures).
• via_measure – the intermediate measure when association_degree > 1.

This table is ideal for:

• Column impact analysis – which measures would be affected by a column change, even through other measures.
• Dependency matrices (tables ↔ measures).
• Visualizing heatmaps of which tables drive the most measures.

You can blend both tables on measure_id in Looker Studio to build rich dashboards.

How it works (high-level)

1. Secure authentication via Azure AD
   You configure the connector with an Azure AD Tenant ID, Client ID, and Client Secret for a service principal that has access to the Power BI Admin API.

2. Power BI Admin API scan
   The connector calls the admin/workspaces/getInfo endpoint to scan the selected workspaces and retrieve workspace/dataset/table/measure metadata.

3. DAX parsing
   Measures’ DAX expressions are parsed using regex patterns to identify:

4. Column references like 'Table Name'[Column Name] or Table[Column].

5. Measure references like [Measure Name].

6. Metadata flattening
   The scan results are transformed into the two logical tables: Measures and Measure–Column Associations.

7. Looker Studio consumption
   Looker Studio requests only the fields needed for a given chart or table; the connector returns data in the standard Community Connector format.

Who is it for?

• Power BI administrators – track model complexity and standardize modeling practices across workspaces.
• BI engineers / semantic modelers – understand dependencies before refactoring tables, columns, or measures.
• Analytics leads – build documentation and governance dashboards that stay up to date with your Power BI environment.

Because the connector only works with metadata, it’s particularly suitable for organizations that want increased transparency without exposing row-level business data.

Security & privacy at a glance

• Uses service principal / client credentials (no per-user tokens).
• Operates only against Power BI Admin API metadata endpoints.
• Does not retrieve row-level fact data.
• Credentials are stored only in Looker Studio’s data source configuration and used by Apps Script at runtime.
• No external database or custom backend is used – execution occurs entirely within Google’s Apps Script/Looker Studio infrastructure.

For more details, see:

• Privacy Policy

Getting started

1. Set up an Azure AD application (service principal)
2. Register an app in Azure AD.
3. Grant it the necessary Power BI Admin API permissions (and admin consent).

4. Create a client secret.

5. Deploy the connector (Apps Script)

6. Add the connector code to an Apps Script project.
7. Configure appsscript.json with your metadata (name, logo, URLs, etc.).

8. Deploy as a Community Connector and copy the deployment ID.

9. Create a Looker Studio data source

10. In Looker Studio, choose Build your own and paste the deployment ID.
11. Fill in Tenant ID, Client ID, Client Secret, and workspace IDs.

12. Choose the Measures output table.

13. Optionally, create a second data source

14. Repeat the step above, but set Output Table to Measure-Column Associations.

15. Build your metadata report

16. Use the measures table to list all measures and their DAX.
17. Use the associations table to analyze measure dependencies on tables/columns.
18. Blend on measure_id for advanced views.

Pricing

The Power BI Metadata connector is currently offered as FREE to use.
(You may still incur costs from underlying platforms such as Power BI, Azure AD, or Google Workspace.)