fabric-automation-bundles 1.0.0b1

Declarative project definitions for Microsoft Fabric

Fabric Automation Bundles

v1.0.0-beta — 30 item types verified against live Fabric API. Production-ready for core workflows. See what's tested.

Declarative project definitions for Microsoft Fabric.

Define your entire Fabric project in a single fabric.yml — lakehouses, notebooks, pipelines, semantic models, Data Agents, security roles, and environment targets — then validate, plan, and deploy with a single command.

fab-bundle init --template medallion --name my-project
fab-bundle validate
fab-bundle plan
fab-bundle deploy -t prod

CLI naming: The standalone CLI is fab-bundle. The long-term goal is integration as a fab bundle subcommand in the Fabric CLI. Both syntaxes are shown in this documentation — use whichever applies to your installation.

The Problem

Microsoft Fabric has no single declarative project definition. The Fabric CLI can export/import items, fabric-cicd can deploy across workspaces, and Terraform can provision infrastructure — but none of them describe:

• What resources your project needs (lakehouses, notebooks, pipelines, semantic models, Data Agents)
• How those resources depend on each other
• How configuration varies across environments (dev/staging/prod)
• What security roles and permissions are required
• How to deploy everything in the correct order

Fabric Automation Bundles fills that gap.

Quick Start

Install

pip install fabric-automation-bundles

Create a New Project

# Medallion lakehouse architecture (bronze/silver/gold)
fab-bundle init --template medallion --name my-analytics

# OSDU + Fabric for Oil, Gas & Energy
fab-bundle init --template osdu_analytics --name my-osdu-project

Or Generate from an Existing Workspace

fab-bundle generate --workspace "My Existing Workspace"

This scans the workspace and produces a fabric.yml you can customize — the fastest on-ramp for existing projects.

Validate

fab-bundle validate

Validates all resource references, dependency chains, and target configurations.

Plan (Dry-Run)

fab-bundle plan -t dev

Shows exactly what would change:

Deployment Plan: my-analytics
  Target:    dev
  Workspace: my-analytics-dev

  +  bronze-lakehouse      Lakehouse      create    New resource
  +  silver-lakehouse      Lakehouse      create    New resource
  +  gold-lakehouse        Lakehouse      create    New resource
  +  spark-env             Environment    create    New resource
  +  etl-bronze            Notebook       create    New resource
  +  etl-silver            Notebook       create    New resource
  +  daily-refresh         DataPipeline   create    New resource
  ~  analytics-model       SemanticModel  update    Definition updated

  Summary: 7 to create, 1 to update

Deploy

fab-bundle deploy -t dev        # Deploy to dev (default)
fab-bundle deploy -t staging    # Deploy to staging
fab-bundle deploy -t prod -y   # Deploy to prod (skip confirmation)

Destroy

fab-bundle destroy -t dev       # Tear down dev environment

The fabric.yml Format

bundle:
  name: my-analytics
  version: "1.0.0"

workspace:
  capacity_id: "your-fabric-capacity-guid"

resources:
  environments:
    spark-env:
      runtime: "1.3"
      libraries: [semantic-link-labs]

  lakehouses:
    bronze:
      description: "Raw data landing zone"
    gold:
      description: "Business-ready datasets"

  notebooks:
    etl-pipeline:
      path: ./notebooks/etl.py
      environment: spark-env
      default_lakehouse: bronze

  pipelines:
    daily-refresh:
      schedule:
        cron: "0 6 * * *"
        timezone: America/Chicago
      activities:
        - notebook: etl-pipeline

  semantic_models:
    analytics-model:
      path: ./semantic_model/
      default_lakehouse: gold

  reports:
    dashboard:
      path: ./reports/dashboard/
      semantic_model: analytics-model

  data_agents:
    my-agent:
      sources: [gold]
      instructions: ./agent/instructions.md
      few_shot_examples: ./agent/examples.yaml

security:
  roles:
    - name: engineers
      entra_group: sg-data-eng
      workspace_role: contributor
    - name: analysts
      entra_group: sg-analysts
      workspace_role: viewer

targets:
  dev:
    default: true
    workspace:
      name: my-analytics-dev
      capacity_id: "your-dev-capacity-guid"

  prod:
    workspace:
      name: my-analytics-prod
    run_as:
      service_principal: sp-fabric-prod

How It Works

Dependency Resolution

Fabric Automation Bundles automatically determines deployment order using topological sorting. You never have to think about what goes first:

environments → lakehouses → notebooks → pipelines
                          → warehouses
                          → semantic_models → reports
                          → data_agents

Variable Substitution

Use ${var.name} in any string value:

variables:
  adme_endpoint:
    description: "ADME endpoint"
    default: "https://dev.energy.azure.com"

targets:
  prod:
    variables:
      adme_endpoint: "https://prod.energy.azure.com"

Include Files

Split large bundles across multiple files:

include:
  - resources/notebooks.yml
  - resources/pipelines.yml
  - security.yml

Developer Workflow & CI/CD Architecture

flowchart TB
    subgraph local["🖥️ Local Development"]
        A["Author fabric.yml\n+ notebooks, SQL, etc."] --> B["fab-bundle validate"]
        B --> C["fab-bundle plan -t dev"]
        C --> D["fab-bundle deploy -t dev"]
        D --> E["fab-bundle drift"]
        E -.->|"iterate"| A
        D --> F["git commit + push"]
    end

    subgraph cicd["⚙️ CI/CD Pipeline"]
        G["PR Opened"] --> H["fab-bundle validate"]
        H --> I["fab-bundle plan -t staging"]
        I --> J{Merge to main}
        J --> K["fab-bundle deploy -t staging -y"]
        K --> L{Approval Gate}
        L --> M["fab-bundle deploy -t prod -y"]
    end

    subgraph fabric["☁️ Microsoft Fabric"]
        direction LR
        DEV["Dev Workspace\n─────────────\nLakehouses\nNotebooks\nPipelines\nWarehouses\nSemantic Models\nReports\nData Agents"]
        STG["Staging Workspace\n─────────────\nLakehouses\nNotebooks\nPipelines\nWarehouses\nSemantic Models\nReports\nData Agents"]
        PRD["Prod Workspace\n─────────────\nLakehouses\nNotebooks\nPipelines\nWarehouses\nSemantic Models\nReports\nData Agents"]
    end

    F --> G
    D -.->|"Fabric REST API"| DEV
    K -.->|"Service Principal"| STG
    M -.->|"Service Principal"| PRD

    style local fill:#1a1a2e,stroke:#16213e,color:#e0e0e0
    style cicd fill:#0f3460,stroke:#16213e,color:#e0e0e0
    style fabric fill:#533483,stroke:#16213e,color:#e0e0e0
    style DEV fill:#2d6a4f,stroke:#1b4332,color:#e0e0e0
    style STG fill:#e9c46a,stroke:#f4a261,color:#1a1a2e
    style PRD fill:#e76f51,stroke:#f4a261,color:#1a1a2e

How fab-bundle fits in the pipeline

Stage	Command	What happens
Local dev	fab-bundle validate	Schema validation, reference checks, dependency resolution
Local dev	fab-bundle plan -t dev	Connects to Fabric, diffs desired vs actual state
Local dev	fab-bundle deploy -t dev	Creates/updates resources in dev workspace
Local dev	fab-bundle drift	Detects out-of-band changes made in the portal
PR check	fab-bundle validate	Gate: blocks merge if bundle is invalid
PR check	fab-bundle plan -t staging	Informational: shows what the merge will change
CI deploy	fab-bundle deploy -t staging -y	Auto-deploys on merge, service principal auth
CI deploy	fab-bundle deploy -t prod -y	Deploys after manual approval gate

GitHub Actions

Copy cicd/github-actions.yml to .github/workflows/fabric-bundle.yml:

- name: Deploy to Fabric
  run: |
    pip install fabric-automation-bundles
    fab-bundle deploy -t prod -y
  env:
    AZURE_TENANT_ID: ${{ secrets.AZURE_TENANT_ID }}
    AZURE_CLIENT_ID: ${{ secrets.AZURE_CLIENT_ID }}
    AZURE_CLIENT_SECRET: ${{ secrets.AZURE_CLIENT_SECRET }}

Azure DevOps

Copy cicd/azure-devops.yml to your repo as a YAML pipeline — includes validate, staging, and production stages with approval gates.

CLI Reference

Command	Description
fab-bundle init	Create a new project from a template
fab-bundle validate	Validate the bundle definition
fab-bundle plan	Preview changes (dry-run)
fab-bundle deploy	Deploy to a target workspace
fab-bundle destroy	Tear down bundle resources
fab-bundle generate	Generate fabric.yml from existing workspace
fab-bundle run <resource>	Run a notebook or pipeline
fab-bundle list	List available templates
fab-bundle bind	Bind an existing workspace item
fab-bundle drift	Detect drift between deployed state and live workspace

Common Flags

Flag	Description
-f, --file	Path to fabric.yml (default: auto-detect)
-t, --target	Target environment (dev, staging, prod)
-y, --auto-approve	Skip confirmation prompts
--dry-run	Preview without making changes

Templates

medallion

Bronze/Silver/Gold lakehouse architecture with:

• Three lakehouses with ETL notebooks
• Data pipeline with dependency chaining
• Semantic model and dashboard
• Data Agent with few-shot examples
• Security roles for engineers and analysts
• Dev/Staging/Prod targets

osdu_analytics

OSDU on Fabric for Oil, Gas & Energy:

• ADME integration with OSDU Search API ingestion
• Well/Wellbore/Production entity flattening
• SQL views for BI (well master, production trends, field rollups)
• Data Agent with petroleum engineering context
• Industry-specific few-shot examples (GOR, water cut, decline analysis)
• ADME connection config per environment

Custom Templates

Create your own templates by adding a directory to fab_bundle/templates/ with a template.yml and a fabric.yml.

Supported Resource Types

45 item types across all Fabric workloads:

Category	Types
Data Engineering	Lakehouse, Notebook, Environment, SparkJobDefinition, GraphQLApi, SnowflakeDatabase
Data Factory	DataPipeline, CopyJob, MountedDataFactory, ApacheAirflowJob, dbt Job
Data Warehouse	Warehouse, SQLDatabase, MirroredDatabase, MirroredWarehouse, MirroredDatabricksCatalog, CosmosDB, Datamart
Power BI	SemanticModel, Report, PaginatedReport, Dashboard, Dataflow
Data Science	MLModel, MLExperiment
Real-Time Intelligence	Eventhouse, Eventstream, KQLDatabase, KQLDashboard, KQLQueryset, Reflex, DigitalTwinBuilder, DigitalTwinBuilderFlow, EventSchemaSet, GraphQuerySet
AI & Knowledge	DataAgent, OperationsAgent, AnomalyDetector, Ontology
Other	VariableLibrary, UserDataFunction, Graph, GraphModel, Map, HLSCohort

Plus OneLake Shortcuts (ADLS, S3, cross-workspace) as lakehouse sub-resources.

See the Resource Types Guide for full details.

Authentication

Fabric Automation Bundles uses azure-identity for authentication:

# Interactive (development)
az login
fab-bundle deploy -t dev

# Service Principal (CI/CD)
export AZURE_TENANT_ID=...
export AZURE_CLIENT_ID=...
export AZURE_CLIENT_SECRET=...
fab-bundle deploy -t prod -y

VS Code Integration

Get autocomplete and validation for fabric.yml by adding a .vscode/settings.json:

{
    "yaml.schemas": {
        "./fabric.schema.json": "fabric.yml"
    }
}

Requires the YAML extension.

Architecture

fab_bundle/
├── cli.py                 # Click CLI (init, validate, plan, deploy, destroy, generate, run, drift)
├── models/
│   └── bundle.py          # 30+ Pydantic models for fabric.yml schema
├── engine/
│   ├── loader.py          # YAML parser with includes + variable substitution
│   ├── resolver.py        # Topological dependency sort
│   ├── planner.py         # Diff engine (desired state vs workspace state)
│   ├── deployer.py        # Executes plans via Fabric REST API
│   ├── state.py           # Deployment state tracking + drift detection
│   └── secrets.py         # Secrets resolution (env vars + Azure KeyVault)
├── providers/
│   └── fabric_api.py      # Fabric REST API client (workspace, items, git, connections, jobs)
├── generators/
│   ├── reverse.py         # Generate fabric.yml from existing workspace
│   └── templates.py       # Template engine with Jinja2
└── templates/
    ├── medallion/          # Bronze/Silver/Gold template
    └── osdu_analytics/     # OSDU + Fabric for OGE

Contributing

Contributions welcome. See CONTRIBUTING.md for details.

git clone https://github.com/dereknguyenio/fabric-automation-bundles.git
cd fabric-automation-bundles
pip install -e ".[dev]"
pytest

Tested Item Types

30 item types verified against a live Fabric workspace:

Status	Item Types
Verified (30)	Lakehouse, Notebook, DataPipeline, Warehouse, Environment, DataAgent, Eventhouse, KQLDatabase, KQLDashboard, KQLQueryset, Eventstream, Reflex, MLModel, MLExperiment, SparkJobDefinition, GraphQLApi, CopyJob, ApacheAirflowJob, Ontology, VariableLibrary, SQLDatabase, CosmosDBDatabase, MirroredAzureDatabricksCatalog, OperationsAgent, AnomalyDetector, DigitalTwinBuilder, GraphQuerySet, GraphModel, Map, UserDataFunction
Capacity-gated (4)	DataBuildToolJob, Graph, HLSCohort, EventSchemaSet
Needs config (2)	SnowflakeDatabase, DigitalTwinBuilderFlow
List-only (5)	Datamart, Dashboard, MirroredWarehouse, PaginatedReport, Dataflow
Needs definition files (4)	SemanticModel (TMDL), Report (PBIR), MirroredDatabase, MountedDataFactory

Feature Stability

Feature	Status	Notes
validate, plan, deploy, destroy	Stable	Tested end-to-end against live API
drift, status, diff, history, doctor	Stable	Tested against live workspaces
run (notebooks/pipelines)	Stable	Job submission works, LRO tracking limited
Security roles (workspace)	Stable	Entra user/group GUIDs
Incremental deploy (hash-based)	Stable	Skips unchanged resources
Deployment locking	Stable	Local + remote (blob lease)
CI/CD (GitHub Actions)	Stable	Proven end-to-end
Remote state (OneLake, Blob, ADLS)	Beta	Built, not yet tested live
MCP server	Beta	12 tools verified locally
OneLake data access roles	Beta	Built, not yet tested live
Environment publish (libraries)	Beta	Fire-and-forget, can't track completion
watch, promote, canary	Experimental	Built, untested
Notifications (Slack/Teams)	Experimental	Built, untested
Policy enforcement	Experimental	Built, untested
Shortcut transformations	Experimental	Model defined, API untested

License

MIT