microsoft-fabric

name	Microsoft Fabric
description	Microsoft Fabric workspace management, governance, REST API patterns, and medallion architecture implementation

Microsoft Fabric Governance Skill

⚠️ Staleness Watch (Last validated: Feb 2026 — REST API v1): Microsoft Fabric ships major features monthly. Monitor the Fabric release notes for new item types, API surface changes, and Git integration improvements. The REST API base URL (api.fabric.microsoft.com/v1) is stable but new endpoints are added regularly.

Overview

Expert knowledge for Microsoft Fabric workspace management, governance, and documentation. Covers REST API patterns, medallion architecture implementation, permission compliance pipelines, and automated workspace inspection.

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Data Project Scaffolding

Recommended Folder Structure

data-project/
├── .github/
│   ├── copilot-instructions.md    # Data project context
│   └── prompts/
│       └── pipeline-review.prompt.md
├── docs/
│   ├── DATA-PLAN.md               # Project scope, objectives
│   ├── DATA-DICTIONARY.md         # Field definitions
│   ├── LINEAGE.md                 # Data flow documentation
│   └── architecture/
│       └── medallion-design.md
├── pipelines/
│   ├── bronze/                     # Raw ingestion
│   │   └── [source]-ingest.py
│   ├── silver/                     # Cleansing, standardization
│   │   └── [entity]-transform.py
│   └── gold/                       # Business logic, aggregations
│       └── [domain]-model.py
├── notebooks/
│   ├── exploration/               # EDA notebooks
│   └── prototypes/                # Pipeline prototypes
├── schemas/
│   ├── bronze/
│   ├── silver/
│   └── gold/
├── tests/
│   ├── unit/
│   └── data-quality/
├── config/
│   ├── connections.yaml
│   └── environments/
│       ├── dev.yaml
│       └── prod.yaml
└── README.md

DATA-PLAN.md Template

# Data Plan: [Project Name]

## Objective
[What business problem does this data pipeline solve?]

## Data Sources

| Source | Type | Frequency | Volume |
|--------|------|-----------|--------|
| [System A] | [API/Database/File] | [Daily/Hourly/Real-time] | [~X records/day] |

## Medallion Architecture

| Layer | Description | Key Transformations |
|-------|-------------|---------------------|
| Bronze | Raw ingestion from [sources] | Minimal: schema enforcement, timestamps |
| Silver | Cleansed, standardized | Deduplication, type casting, validation |
| Gold | Business-ready | Joins, aggregations, business logic |

## Data Quality Rules

| Rule | Layer | Implementation |
|------|-------|----------------|
| No nulls in [field] | Silver | Validation check |
| Referential integrity | Gold | Foreign key check |

## Success Criteria
- [ ] All sources ingesting to Bronze
- [ ] Silver quality checks passing
- [ ] Gold tables serving [consumers]
- [ ] Documentation complete

DATA-DICTIONARY.md Template

# Data Dictionary: [Dataset Name]

## Tables

### [table_name]
**Layer**: Gold
**Description**: [What this table represents]
**Update Frequency**: [Daily/Hourly]
**Primary Key**: [field]

| Field | Type | Description | Nullable | Example |
|-------|------|-------------|----------|---------|
| id | STRING | Unique identifier | No | "abc123" |
| created_at | TIMESTAMP | Record creation time | No | 2026-02-07T10:00:00Z |
| [field] | [TYPE] | [Description] | [Yes/No] | [Example] |

**Business Rules**:
- [Rule 1]
- [Rule 2]

**Lineage**: Bronze.[source_table] → Silver.[cleaned_table] → Gold.[table_name]

copilot-instructions.md Template (Data Projects)

# [Project Name] — Data Engineering Context

## Overview
[What data problem this project solves]

## Current Phase
- [ ] Source analysis
- [ ] Bronze layer
- [ ] Silver layer
- [ ] Gold layer
- [ ] Documentation

## Key Files
- Data plan: docs/DATA-PLAN.md
- Data dictionary: docs/DATA-DICTIONARY.md
- Lineage: docs/LINEAGE.md

## Alex Guidance
- **Platform**: [Fabric/Databricks/Snowflake/etc.]
- **Language**: [Python/SQL/Spark]
- Follow medallion architecture patterns
- Document data quality rules
- Include idempotency in pipeline designs

## Conventions
- Table naming: [layer]_[domain]_[entity]
- Pipeline naming: [source]_to_[target]_[frequency]
- Use incremental loads where possible

## Don't
- Don't modify Bronze layer (append-only)
- Don't hardcode connection strings
- Don't skip data quality checks

---

Activation Triggers

• "Fabric workspace", "Fabric API", "Fabric governance"
• "medallion architecture", "bronze silver gold", "lakehouse"
• "Unity Catalog", "schema-enabled lakehouse"
• "permission pipeline", "contact compliance"
• "workspace inventory", "Fabric scanner"

Knowledge Domains

1. Microsoft Fabric REST API

Endpoint Structure:

Base URL: https://api.fabric.microsoft.com/v1
Workspaces: /workspaces/{workspaceId}
Items: /workspaces/{workspaceId}/items?type={type}
Definitions: /workspaces/{workspaceId}/{type}/{itemId}/getDefinition

Async Operation Pattern:

1. POST to definition endpoint
2. Receive 202 Accepted with Location header
3. Poll Location URL until status = Succeeded
4. GET /result endpoint for payload
5. Decode base64 content for notebooks

Authentication:

# Fabric API token
$fabricToken = az account get-access-token --resource https://api.fabric.microsoft.com --query accessToken -o tsv

# Unity Catalog token (for schema-enabled lakehouses)
$storageToken = az account get-access-token --resource https://storage.azure.com --query accessToken -o tsv

2. Medallion Architecture

Layer Design:

Layer	Purpose	Lakehouse Type	Schema
Bronze	Raw ingestion	Standard	Default
Silver	Cleansed data	Standard	Default
Gold	Domain models	Schema-enabled	dbo, CxPulse

Table Distribution Pattern (Fishbowl Example):

• Bronze: 88 tables (raw data from 5+ systems)
• Silver: 77 tables (cleansed, standardized)
• Gold: 39 tables (domain-separated)

Key Insight: Gold layer benefits from schema-enabled lakehouses for domain organization while Bronze/Silver remain standard for ingestion flexibility.

3. Permission Compliance Pipeline

7-Step CPM Pattern:

%%{init: {'theme': 'base', 'themeVariables': { 'edgeLabelBackground':'#ffffff', 'lineColor': '#57606a' }}}%%
graph LR
    A["📥 Copy<br/>Contacts"] --> B["🔐 MSWide<br/>Perm API"]
    B --> C["🔐 TXN<br/>Perm API"]
    C --> D["✅ CPERM<br/>Validation"]
    D --> E["🌍 Country<br/>Enrichment"]
    E --> F["💧 Data<br/>Hydration"]
    F --> G["📤 Load<br/>Lakehouse"]

    style A fill:#ddf4ff,color:#0550ae,stroke:#80ccff
    style B fill:#d8b9ff,color:#6639ba,stroke:#bf8aff
    style C fill:#d8b9ff,color:#6639ba,stroke:#bf8aff
    style D fill:#d3f5db,color:#1a7f37,stroke:#6fdd8b
    style E fill:#fff8c5,color:#9a6700,stroke:#d4a72c
    style F fill:#ddf4ff,color:#0550ae,stroke:#80ccff
    style G fill:#d3f5db,color:#1a7f37,stroke:#6fdd8b

    linkStyle default stroke:#57606a,stroke-width:1.5px

Permission Tables:

• CPERM_OptOut - Marketing opt-outs
• US_FAR_List - US government exclusions
• US_CAPSL_List - CAPSL compliance
• SpamHaus_List - Known bad actors

4. Unity Catalog Integration

Schema-Enabled Lakehouse Access:

# Catalog URL pattern
catalog_url = f"https://onelake.table.fabric.microsoft.com/delta/{workspace_id}/{lakehouse_id}"

# List schemas
GET /schemas

# List tables in schema
GET /schemas/{schema_name}/tables

Domain Organization:

• Use schemas for domain separation (dbo, CxPulse)
• Enable for Gold layer business domains
• Keep Bronze/Silver as standard for flexibility

5. PowerShell Automation

Workspace Scanner Pattern:

# Use Invoke-WebRequest for header access (not Invoke-RestMethod)
$response = Invoke-WebRequest -Uri $uri -Headers $headers -Method Post

# Handle 202 Accepted
if ($response.StatusCode -eq 202) {
    $operationUrl = $response.Headers["Location"][0]

    # Poll for completion
    do {
        Start-Sleep -Seconds 2
        $status = Invoke-RestMethod -Uri $operationUrl -Headers $headers
    } while ($status.status -ne "Succeeded")

    # Get result
    $result = Invoke-RestMethod -Uri "$operationUrl/result" -Headers $headers
}

Documentation Patterns

Inventory Structure

1. Executive Summary - Metrics and architecture overview
2. Mermaid Diagrams - Visual architecture representation
3. Item Inventory - Complete list with IDs
4. Table Catalog - All tables by lakehouse
5. Code Analysis - Notebook/pipeline functions
6. Data Flow - End-to-end lineage

Recommended Diagrams

• Data flow (C4 or flowchart)
• Data lineage (graph)
• Lakehouse architecture (container)
• Permission pipeline (sequence)
• Monitoring topology (deployment)

Best Practices

Workspace Governance

1. Naming Conventions: Use prefixes (Bronze_, Silver_, Gold_)
2. Schema Separation: Domain-specific schemas in Gold layer
3. Permission Layers: Sequential filtering for compliance
4. Documentation: Maintain inventory with actual IDs

API Usage

1. Token Caching: Refresh every 60 minutes
2. Rate Limiting: Implement exponential backoff
3. Async Handling: Always check for 202 responses
4. Error Recovery: Log operation URLs for retry

Medallion Implementation

1. Bronze: Accept all formats, minimal transformation
2. Silver: Standardize types, deduplicate, validate
3. Gold: Domain models, business logic, aggregations

Synapses

{
  "version": "1.0",
  "skill": "microsoft-fabric",
  "connections": [
    {
      "target": "medallion-architecture",
      "type": "Implements",
      "strength": "Critical"
    },
    {
      "target": "data-governance",
      "type": "Enables",
      "strength": "Critical"
    },
    {
      "target": "powershell-automation",
      "type": "Integrates",
      "strength": "High"
    },
    {
      "target": 
       
       
    
    
       
       
       
    
  

Related Resources

• Microsoft Fabric REST API Documentation
• Unity Catalog API
• Medallion Architecture

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Skill created from Fishbowl workspace inspection session, February 2026