Resource Types¶

This page is the reference for every Fabric item type that udp-cicd can manage: the type key used in udp.yml, the underlying Fabric API type, the accepted definition formats, and a full YAML example for each resource. udp-cicd covers 46 Fabric item types, of which 30 are verified against live Fabric workspaces.

Beyond Fabric — Entra & Azure

A single udp.yml can also declare Microsoft Entra directory objects and Azure (ARM) resources alongside Fabric items. Those types are documented in §5 and in the dedicated Multi-platform guide.

1. Coverage and deployment order¶

udp-cicd supports 46 Fabric item types. 30 of these are verified live; the remainder are defined against the published Fabric REST API and pending live verification. A small number of types are list-only because the Fabric API does not support creating them.

Resources deploy in dependency order, resolved automatically by the engine:

Order	Rule
1	Environments deploy before lakehouses
2	Lakehouses deploy before notebooks, warehouses, and semantic models
3	Notebooks deploy before pipelines
4	Semantic models deploy before reports and data agents

You do not declare this ordering in udp.yml; the Resolver derives it from resource references (for example, a notebook's default_lakehouse or a report's semantic_model).

Workspace folders by type

Every resource type belongs to a workspace folder category (Notebooks, Pipelines, Lakehouses, Models, Databases, Real-Time, …). Set workspace.folders_by_type: true to have deploy group items into those folders automatically.

2. Supported types¶

2.1 Data Engineering¶

Resource	Type Key	API Type	Definitions
Lakehouse	`lakehouses`	Lakehouse	Shortcuts, tables, schemas
Notebook	`notebooks`	Notebook	.py, .ipynb, .sql, .scala, .r
Environment	`environments`	Environment	Runtime, libraries, Spark config
Spark Job Definition	`spark_job_definitions`	SparkJobDefinition	.py, .jar
GraphQL API	`graphql_apis`	GraphQLApi	Schema file
Snowflake Database	`snowflake_databases`	SnowflakeDatabase	Connection-based
Materialized Lake View	`materialized_lake_views`	MaterializedLakeView	SQL definition

2.2 Data Factory¶

Resource	Type Key	API Type	Definitions
Data Pipeline	`pipelines`	DataPipeline	YAML activities or JSON
Copy Job	`copy_jobs`	CopyJob	JSON definition
Mounted Data Factory	`mounted_data_factories`	MountedDataFactory	Metadata
Apache Airflow Job	`airflow_jobs`	ApacheAirflowJob	DAG file
dbt Job	`dbt_jobs`	DataBuildToolJob	dbt project

2.3 Data Warehouse¶

Resource	Type Key	API Type	Definitions
Warehouse	`warehouses`	Warehouse	SQL scripts
SQL Database	`sql_databases`	SQLDatabase	SQL scripts
Mirrored Database	`mirrored_databases`	MirroredDatabase	Connection-based
Mirrored Warehouse	`mirrored_warehouses`	MirroredWarehouse	List-only; cannot be created via API
Mirrored Databricks Catalog	`mirrored_databricks_catalogs`	MirroredAzureDatabricksCatalog	Connection-based
Cosmos DB Database	`cosmosdb_databases`	CosmosDBDatabase	Connection-based
Datamart	`datamarts`	Datamart	List-only; cannot be created via API

2.4 Power BI¶

Resource	Type Key	API Type	Definitions
Semantic Model	`semantic_models`	SemanticModel	TMDL or TMSL
Report	`reports`	Report	PBIR format
Paginated Report	`paginated_reports`	PaginatedReport	List-only; cannot be created via API
Dashboard	`dashboards`	Dashboard	List-only; cannot be created via API
Dataflow	`dataflows`	Dataflow	Not supported by Fabric API

2.5 Data Science¶

Resource	Type Key	API Type	Definitions
ML Model	`ml_models`	MLModel	MLflow model
ML Experiment	`ml_experiments`	MLExperiment	Metadata

2.6 Real-Time Intelligence¶

Resource	Type Key	API Type	Definitions
Eventhouse	`eventhouses`	Eventhouse	KQL scripts
Eventstream	`eventstreams`	Eventstream	JSON definition
KQL Database	`kql_databases`	KQLDatabase	KQL scripts
KQL Dashboard	`kql_dashboards`	KQLDashboard	Definition file
KQL Queryset	`kql_querysets`	KQLQueryset	Definition file
Reflex (Data Activator)	`reflex`	Reflex	JSON definition
Digital Twin Builder	`digital_twin_builders`	DigitalTwinBuilder	Definition file
Digital Twin Builder Flow	`digital_twin_builder_flows`	DigitalTwinBuilderFlow	Definition file
Event Schema Set	`event_schema_sets`	EventSchemaSet	Definition file
Graph Query Set	`graph_query_sets`	GraphQuerySet	Definition file

2.7 AI & Knowledge¶

Resource	Type Key	API Type	Definitions
Data Agent	`data_agents`	DataAgent	Instructions + examples
Operations Agent	`operations_agents`	OperationsAgent	Instructions
Anomaly Detector	`anomaly_detectors`	AnomalyDetector	Configuration
Ontology	`ontologies`	Ontology	Definition file

2.8 Other¶

Resource	Type Key	API Type	Definitions
Variable Library	`variable_libraries`	VariableLibrary	Key-value pairs
User Data Function	`user_data_functions`	UserDataFunction	Function definition
Graph	`graphs`	Graph	Definition file
Graph Model	`graph_models`	GraphModel	Definition file
Map	`map_items`	Map	Definition file
HLS Cohort	`hls_cohorts`	HLSCohort	Definition file

3. OneLake shortcuts¶

Shortcuts are not a separate item type; they are sub-resources of lakehouses.

lakehouses:
  bronze_lakehouse:
    shortcuts:
      - name: external_data
        target: "adls://storageaccount/container/path"
        path: Tables
        connection_id: "optional-connection-guid"
      - name: s3_data
        target: "s3://bucket-name/prefix"
      - name: cross_workspace
        target: "onelake://workspace-id/item-id/Tables/my_table"

Supported shortcut targets:

Scheme	Target
`adls://`	Azure Data Lake Storage Gen2
`s3://`	Amazon S3
`onelake://`	Cross-workspace OneLake reference

3.1 Shortcut transformations¶

Transformations auto-convert source files to managed Delta tables that stay in sync with the source, without pipelines.

File transformations convert CSV, Parquet, JSON, or Excel files into Delta tables:

lakehouses:
  bronze_lakehouse:
    shortcuts:
      - name: csv_sales_data
        target: "adls://datalake/sales/*.csv"
        path: Files
        transformation:
          type: file
          source_format: csv
          destination_table: raw_sales
          sync: true
          flatten: false

      - name: nested_json_events
        target: "adls://datalake/events/*.json"
        path: Files
        transformation:
          type: file
          source_format: json
          destination_table: raw_events
          flatten: true
          compression: gzip

      - name: excel_reports
        target: "adls://datalake/finance/*.xlsx"
        path: Files
        transformation:
          type: file
          source_format: excel
          destination_table: finance_reports

AI-powered transformations apply summarization, translation, or classification:

lakehouses:
  documents_lakehouse:
    shortcuts:
      - name: support_tickets
        target: "adls://datalake/tickets/*.json"
        path: Files
        transformation:
          type: ai
          ai_skill: summarize
          destination_table: ticket_summaries

      - name: multilingual_docs
        target: "adls://datalake/docs/*.json"
        path: Files
        transformation:
          type: ai
          ai_skill: translate
          ai_prompt: "Translate to English"
          destination_table: docs_english

      - name: email_classification
        target: "adls://datalake/emails/*.json"
        path: Files
        transformation:
          type: ai
          ai_skill: classify
          ai_prompt: "Classify as: complaint, inquiry, feedback, spam"
          destination_table: classified_emails

4. YAML reference: all resource types¶

4.1 Data Engineering¶

Lakehouse¶

lakehouses:
  bronze_lakehouse:
    description: "Raw data landing zone"
    enable_schemas: true
    tables:
      raw_orders:
        schema_path: ./schemas/orders.json
        partition_by: [order_date]
    shortcuts:
      - name: external_data
        target: "adls://account/container/path"
        path: Tables
        connection_id: "optional-guid"
        transformation:
          type: file
          source_format: csv
          destination_table: raw_external

Notebook¶

notebooks:
  etl_pipeline:
    path: ./notebooks/etl.py
    description: "ETL pipeline"
    environment: spark_env
    default_lakehouse: bronze_lakehouse
    parameters:
      batch_size: 1000
      source_table: orders
    folder: ETL/Bronze

Environment¶

environments:
  spark_env:
    runtime: "1.3"
    libraries:
      - semantic-link-labs
      - delta-spark
    conda_dependencies:
      - numpy=1.24
    spark_properties:
      spark.sql.shuffle.partitions: "200"

Spark Job Definition¶

spark_job_definitions:
  distributed_training:
    path: ./spark_jobs/train.py
    description: "Distributed model training"
    environment: spark_env
    default_lakehouse: feature_store
    args: ["--epochs", "10", "--batch-size", "256"]
    conf:
      spark.executor.memory: "8g"
      spark.executor.cores: "4"

GraphQL API¶

graphql_apis:
  product_api:
    description: "GraphQL API over product data"
    path: ./graphql/schema.graphql
    data_source: gold_lakehouse

Snowflake Database¶

snowflake_databases:
  snowflake_mirror:
    description: "Mirrored Snowflake data"
    connection: snowflake_conn

Materialized Lake View¶

materialized_lake_views:
  device_daily_summary:
    description: "Daily per-device telemetry rollup"
    path: ./sql/device_daily_summary.sql
    default_lakehouse: telemetry_lakehouse
    refresh_cron: "0 2 * * *"

4.2 Data Factory¶

Data Pipeline¶

pipelines:
  daily_refresh:
    description: "Daily ETL pipeline"
    schedule:
      cron: "0 6 * * *"
      timezone: America/Chicago
      enabled: true
    activities:
      - name: ingest
        notebook: ingest_notebook
      - name: transform
        notebook: transform_notebook
        depends_on: [ingest]
      - name: load
        notebook: load_notebook
        depends_on: [transform]

Copy Job¶

copy_jobs:
  copy_sales_data:
    description: "Copy sales data from Azure SQL"
    path: ./copy_jobs/sales_copy.json

Mounted Data Factory¶

mounted_data_factories:
  legacy_adf:
    description: "Mounted Azure Data Factory for legacy pipelines"
    data_factory_id: "/subscriptions/.../resourceGroups/.../providers/Microsoft.DataFactory/factories/udp-adf"

Apache Airflow Job¶

airflow_jobs:
  airflow_etl:
    description: "Airflow DAG for complex orchestration"
    path: ./dags/etl_dag.py

dbt Job¶

dbt_jobs:
  dbt_transform:
    description: "dbt transformation project"
    path: ./dbt_project/
    environment: spark_env

4.3 Data Warehouse¶

Warehouse¶

warehouses:
  analytics_warehouse:
    description: "SQL analytics warehouse"
    sql_scripts:
      - ./sql/create_views.sql
      - ./sql/create_procedures.sql

SQL Database¶

sql_databases:
  operational_db:
    description: "Operational SQL database"
    sql_scripts:
      - ./sql/schema.sql
      - ./sql/seed_data.sql

Mirrored Database¶

mirrored_databases:
  azure_sql_mirror:
    description: "Mirrored Azure SQL database"
    source_type: "Azure SQL"
    connection: azure_sql_conn

Mirrored Warehouse¶

mirrored_warehouses:
  synapse_mirror:
    description: "Mirrored Synapse warehouse"
    source_type: "Synapse"

Mirrored Databricks Catalog¶

mirrored_databricks_catalogs:
  databricks_catalog:
    description: "Mirrored Databricks Unity Catalog"
    connection: databricks_conn

Cosmos DB Database¶

cosmosdb_databases:
  cosmos_mirror:
    description: "Mirrored Cosmos DB data"
    connection: cosmos_conn

Datamart¶

datamarts:
  sales_datamart:
    description: "Self-service sales datamart"
    path: ./datamarts/sales_definition.json

4.4 Power BI¶

Semantic Model¶

semantic_models:
  analytics_model:
    path: ./semantic_model/
    description: "Semantic model over gold lakehouse"
    default_lakehouse: gold_lakehouse
    auto_refresh: true
    refresh_timeout: 600
    folder: Models

Report¶

reports:
  executive_dashboard:
    path: ./reports/dashboard/
    description: "Executive dashboard (PBIR format)"
    semantic_model: analytics_model
    folder: Reports

Paginated Report¶

paginated_reports:
  monthly_invoice:
    description: "Monthly invoice report (RDL)"
    path: ./reports/invoice.rdl
    data_source: analytics_warehouse

Dashboard¶

dashboards:
  overview_dashboard:
    description: "High-level KPI dashboard"

Dataflow¶

dataflows:
  customer_transform:
    description: "Dataflow Gen2 for customer data"
    path: ./dataflows/customer_transform.json

4.5 Data Science¶

ML Model¶

ml_models:
  churn_model:
    path: ./models/churn_model/
    description: "Customer churn prediction model"
    framework: xgboost

ML Experiment¶

ml_experiments:
  churn_experiment:
    description: "Churn prediction experiment tracking"

4.6 Real-Time Intelligence¶

Eventhouse¶

eventhouses:
  telemetry_eventhouse:
    description: "IoT telemetry eventhouse"
    kql_scripts:
      - ./kql/create_tables.kql
      - ./kql/create_functions.kql
    retention_days: 365
    cache_days: 31

Eventstream¶

eventstreams:
  device_events:
    description: "Real-time device event stream"
    path: ./eventstreams/device_config.json
    sources:
      - type: event_hub
        name: iot-hub-events
    destinations:
      - type: eventhouse
        name: telemetry_eventhouse

KQL Database¶

kql_databases:
  telemetry_db:
    description: "KQL database for device telemetry"
    parent_eventhouse: telemetry_eventhouse
    kql_scripts:
      - ./kql/create_tables.kql

KQL Dashboard¶

kql_dashboards:
  ops_dashboard:
    description: "Real-time operations dashboard"
    path: ./dashboards/ops_dashboard.json
    data_source: telemetry_db

KQL Queryset¶

kql_querysets:
  telemetry_queries:
    description: "Pre-built KQL queries for analysis"
    path: ./kql/querysets/
    data_source: telemetry_db

Reflex (Data Activator)¶

reflex:
  anomaly_alerts:
    description: "Trigger alerts on anomalous readings"
    path: ./reflex/anomaly_rules.json

Digital Twin Builder¶

digital_twin_builders:
  factory_twin:
    description: "Digital twin of factory floor"
    path: ./twins/factory_definition.json

Digital Twin Builder Flow¶

digital_twin_builder_flows:
  factory_flow:
    description: "Data flow for factory twin"
    path: ./twins/factory_flow.json
    twin_builder: factory_twin

Event Schema Set¶

event_schema_sets:
  device_schemas:
    description: "Schema definitions for IoT events"
    path: ./schemas/device_events.json

Graph Query Set¶

graph_query_sets:
  network_queries:
    description: "Graph queries for network analysis"
    path: ./graph/queries/
    data_source: telemetry_db

4.7 AI & Knowledge¶

Data Agent¶

data_agents:
  analytics_agent:
    description: "Natural language interface to your data"
    sources:
      - gold_lakehouse
      - analytics_warehouse
    instructions: ./agent/instructions.md
    few_shot_examples: ./agent/examples.yaml
    tables_in_scope:
      - daily_order_summary
      - customer_360

Operations Agent¶

operations_agents:
  ops_agent:
    description: "Operations monitoring agent"
    sources:
      - telemetry_eventhouse
    instructions: ./agent/ops_instructions.md

Anomaly Detector¶

anomaly_detectors:
  revenue_detector:
    description: "Detect revenue anomalies"
    data_source: gold_lakehouse
    path: ./detectors/revenue_config.json

Ontology¶

ontologies:
  business_ontology:
    description: "Business domain knowledge graph"
    path: ./ontology/definition.json
    data_sources:
      - gold_lakehouse
      - analytics_warehouse

4.8 Other¶

Variable Library¶

variable_libraries:
  shared_config:
    description: "Shared configuration variables"
    variables:
      environment: production
      region: us-east
      log_level: info
      max_retries: "3"

User Data Function¶

user_data_functions:
  custom_transform:
    description: "Custom data transformation function"
    path: ./functions/transform.py
    runtime: python

Graph¶

graphs:
  knowledge_graph:
    description: "Product knowledge graph"
    path: ./graph/definition.json
    data_source: gold_lakehouse

Graph Model¶

graph_models:
  supply_chain_model:
    description: "Supply chain graph model"
    path: ./graph/supply_chain.json
    data_source: analytics_warehouse

Map¶

map_items:
  geo_mapping:
    description: "Geographic data mapping"
    path: ./maps/geo_config.json

HLS Cohort¶

hls_cohorts:
  patient_cohort:
    description: "Patient cohort for clinical analytics"
    path: ./cohorts/patient_definition.json

5. Beyond Fabric: Entra and Azure¶

The same udp.yml can declare resources on two further control planes. Each type is tagged with a platform that selects the provider used to deploy it. Fabric is the default; the types below opt into Entra or Azure. See the Multi-platform guide for the end-to-end walkthrough and the 10-azure-and-entra example.

Platform	Deployed via	Scope	Auth
Fabric	Fabric REST API	Workspace	Fabric token
Entra	Microsoft Graph	Tenant	Graph `*.ReadWrite.All`
Azure	Bicep through `az` CLI	Subscription / resource group	`az login`

5.1 Microsoft Entra¶

The resource key is the object's displayName. Create is idempotent — an existing object of the same display name is patched, not duplicated.

Resource	Type Key	Graph Entity
Security Group	`entra_groups`	group
App Registration	`entra_apps`	application

resources:
  entra_groups:
    sg-analytics-readers:
      description: "Read access to the analytics workload"
      security_enabled: true
      assignable_to_role: false
      owners: []          # display names, UPNs, or object GUIDs
      members: []

  entra_apps:
    analytics-ingest-app:
      sign_in_audience: AzureADMyOrg
      redirect_uris: ["https://localhost/callback"]
      identifier_uris: ["api://analytics-ingest"]
      create_service_principal: true

5.2 Azure (Bicep)¶

Resource groups and storage accounts are emitted as generated Bicep; any other resource type is supplied as an author-written .bicep file via azure_deployments. Subscription and location default from the top-level azure: block and can be overridden per resource.

Resource	Type Key	ARM Type
Resource Group	`azure_resource_groups`	Microsoft.Resources/resourceGroups
Storage Account	`azure_storage_accounts`	Microsoft.Storage/storageAccounts
Generic Bicep	`azure_deployments`	Microsoft.Resources/deployments

In addition, these first-class service types each deploy as a single ARM resource via a generated, resource-group-scope Bicep template. They share the same shape (resource_group, location, optional sku/kind/properties/tags). See the full catalogue in 11-azure-data-services.

Service	Type Key	ARM Type
Data Factory	`azure_data_factories`	Microsoft.DataFactory/factories
Databricks	`azure_databricks_workspaces`	Microsoft.Databricks/workspaces
Databricks Structured Streaming	`azure_databricks_structured_streaming`	Microsoft.Databricks/workspaces
Event Hubs	`azure_event_hub_namespaces`	Microsoft.EventHub/namespaces
Event Grid	`azure_event_grid_topics`	Microsoft.EventGrid/topics
Stream Analytics	`azure_stream_analytics_jobs`	Microsoft.StreamAnalytics/streamingjobs
IoT Hub	`azure_iot_hubs`	Microsoft.Devices/IotHubs
Logic Apps	`azure_logic_apps`	Microsoft.Logic/workflows
Functions	`azure_functions`	Microsoft.Web/sites
Blob Storage	`azure_blob_storage`	Microsoft.Storage/storageAccounts
Data Lake Storage Gen2	`azure_data_lake_storage`	Microsoft.Storage/storageAccounts
Files	`azure_files`	Microsoft.Storage/storageAccounts
Queue Storage	`azure_queue_storage`	Microsoft.Storage/storageAccounts
Table Storage	`azure_table_storage`	Microsoft.Storage/storageAccounts
SQL Database	`azure_sql_databases`	Microsoft.Sql/servers/databases
SQL Managed Instance	`azure_sql_managed_instances`	Microsoft.Sql/managedInstances
SQL Server on VMs	`azure_sql_virtual_machines`	Microsoft.SqlVirtualMachine/sqlVirtualMachines
Database for PostgreSQL	`azure_postgresql`	Microsoft.DBforPostgreSQL/flexibleServers
Database for MySQL	`azure_mysql`	Microsoft.DBforMySQL/flexibleServers
Database for MariaDB	`azure_mariadb`	Microsoft.DBforMariaDB/servers
Cosmos DB	`azure_cosmosdb_accounts`	Microsoft.DocumentDB/databaseAccounts
Cache for Redis	`azure_redis_cache`	Microsoft.Cache/Redis
Data Box	`azure_data_box`	Microsoft.DataBox/jobs

Governance / security / AI

Service	Type Key	ARM Type
Microsoft Purview	`azure_purview_accounts`	Microsoft.Purview/accounts
Key Vault	`azure_key_vaults`	Microsoft.KeyVault/vaults
Azure Policy	`azure_policy_assignments`	Microsoft.Authorization/policyAssignments
Defender for Cloud	`azure_defender_plans`	Microsoft.Security/pricings
Machine Learning	`azure_machine_learning_workspaces`	Microsoft.MachineLearningServices/workspaces
AI Foundry	`azure_ai_foundry`	Microsoft.MachineLearningServices/workspaces
Sentinel	`azure_sentinel`	Microsoft.SecurityInsights/onboardingStates
AI Video Indexer	`azure_video_indexer`	Microsoft.VideoIndexer/accounts
OpenAI	`azure_openai`	Microsoft.CognitiveServices/accounts
Load Testing	`azure_load_testing`	Microsoft.LoadTestService/loadTests
Agent identities	`azure_agent_ids`	Microsoft.ManagedIdentity/userAssignedIdentities
Service Groups	`azure_service_groups`	Microsoft.Management/serviceGroups

Monitoring / observability

Service	Type Key	ARM Type
Monitor (App Insights)	`azure_monitor_components`	Microsoft.Insights/components
Alerts	`azure_alerts`	Microsoft.Insights/metricAlerts
Diagnostic Settings	`azure_diagnostic_settings`	Microsoft.Insights/diagnosticSettings
Log Analytics Workspace	`azure_log_analytics_workspaces`	Microsoft.OperationalInsights/workspaces
Metrics (DCR)	`azure_metrics`	Microsoft.Insights/dataCollectionRules
Workbooks	`azure_workbooks`	Microsoft.Insights/workbooks
Activity Log	`azure_activity_logs`	Microsoft.Insights/diagnosticSettings
Database Watchers	`azure_database_watchers`	Microsoft.DatabaseWatcher/watchers

Networking

Service	Type Key	ARM Type
Network Watcher	`azure_network_watchers`	Microsoft.Network/networkWatchers
DNS Zones	`azure_dns_zones`	Microsoft.Network/dnsZones
Network Interfaces	`azure_network_interfaces`	Microsoft.Network/networkInterfaces
Private DNS Zones	`azure_private_dns_zones`	Microsoft.Network/privateDnsZones
Public IP Address	`azure_public_ip_addresses`	Microsoft.Network/publicIPAddresses
Route Tables	`azure_route_tables`	Microsoft.Network/routeTables
Virtual Network	`azure_virtual_networks`	Microsoft.Network/virtualNetworks
Local Network Gateway	`azure_local_network_gateways`	Microsoft.Network/localNetworkGateways
Peering Services	`azure_peering_services`	Microsoft.Network/peeringServices
Peering	`azure_peerings`	Microsoft.Peering/peerings
Virtual Network Gateway	`azure_virtual_network_gateways`	Microsoft.Network/virtualNetworkGateways
Virtual WAN	`azure_virtual_wans`	Microsoft.Network/virtualWans
DDoS Protection	`azure_ddos_protection_plans`	Microsoft.Network/ddosProtectionPlans
Firewall	`azure_firewalls`	Microsoft.Network/azureFirewalls
IP Groups	`azure_ip_groups`	Microsoft.Network/ipGroups
Network Security Group	`azure_network_security_groups`	Microsoft.Network/networkSecurityGroups
Application Gateway	`azure_application_gateways`	Microsoft.Network/applicationGateways
Application Security Group	`azure_application_security_groups`	Microsoft.Network/applicationSecurityGroups

Catalogue: 12-azure-platform-services.

Storage-family naming & child resources

The five storage-family services deploy as storage accounts, so their names must be 3–24 lowercase alphanumeric. Child resources such as azure_sql_databases may need a parent server — name the database "<server>/<database>" or use azure_deployments for full control.

azure:
  subscription: "${var.subscription_id}"
  location: eastus

resources:
  azure_resource_groups:
    rg-analytics-dev:
      location: eastus
      tags: { env: dev }

  azure_storage_accounts:
    saanalyticsdev01:               # 3–24 lowercase alphanumeric
      resource_group: rg-analytics-dev
      sku: Standard_LRS
      kind: StorageV2
      access_tier: Hot

  azure_deployments:
    keyvault-analytics:
      scope: group                  # or "subscription"
      resource_group: rg-analytics-dev
      template_file: ./bicep/keyvault.bicep
      parameters:
        vaultName: kv-analytics-dev

Workspace-centric orchestration

The deploy engine is still workspace-oriented: a deployment containing only Entra/Azure resources will still create a Fabric workspace. Mixed and Fabric-only deployments are unaffected.