fabric-automation-bundles 0.2.0

Declarative project definitions for Microsoft Fabric — inspired by Databricks Declarative Automation Bundles

Fabric Automation Bundles

Declarative project definitions for Microsoft Fabric — inspired by Databricks Declarative Automation Bundles.

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

Databricks has Declarative Automation Bundles — a single YAML file that defines your entire project and deploys it consistently across dev, staging, and prod.

Microsoft Fabric has nothing equivalent. The Fabric CLI can export/import items, fabric-cicd can deploy across workspaces, and Terraform can provision infrastructure — but there is no single declarative project definition that describes:

• 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 chevron-osdu

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: F64

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: F2

  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

CI/CD Integration

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

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.

Comparison: Databricks vs Fabric Automation Bundles

Feature	Databricks (DABs)	Fabric Automation Bundles
Project definition	databricks.yml	fabric.yml
CLI (standalone)	databricks bundle	fab-bundle
CLI (integrated)	databricks bundle	fab bundle (planned)
Validate	databricks bundle validate	fab-bundle validate
Deploy	databricks bundle deploy	fab-bundle deploy
Dry-run / Plan	databricks bundle deploy	fab-bundle plan
Run a resource	databricks bundle run	fab-bundle run
Generate from existing	databricks bundle generate	fab-bundle generate
Init from template	databricks bundle init	fab-bundle init
Targets/Environments	✅ YAML targets	✅ YAML targets
Dependency ordering	✅ Automatic	✅ Automatic (topological sort)
Variable substitution	✅ ${var.name}	✅ ${var.name}
Include files	✅	✅
Service principal auth	✅	✅
GitHub Actions	✅	✅ (template provided)
Azure DevOps	✅	✅ (template provided)
Workspace security	Via Unity Catalog	✅ Entra + OneLake roles
Data Agents	N/A	✅ First-class resource
Semantic Models	N/A	✅ First-class resource
Custom templates	✅	✅

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

Architecture

fab_bundle/
├── cli.py                 # Click CLI (init, validate, plan, deploy, destroy, generate, run)
├── 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
├── providers/
│   └── fabric_api.py      # Fabric REST API client with retry logic
├── 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/microsoft/fabric-automation-bundles.git
cd fabric-automation-bundles
pip install -e ".[dev]"
pytest

License

MIT