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**Phase 1** provisions Azure infrastructure using Bicep templates with ARM idempotency:
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-**Fabric Capacity** - Dedicated compute resources for Fabric workloads with auto-scaling capabilities
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-**Event Hub** - High-throughput streaming service for real-time data ingestion and event processing
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-**Resource Group** - Logical container organizing and managing all deployed Azure resources
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-**[Resource Group](https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/manage-resource-groups-portal#what-is-a-resource-group)** - Logical container organizing and managing all deployed Azure resources
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-**[Fabric Capacity](https://learn.microsoft.com/en-us/fabric/enterprise/licenses#capacity)** - Dedicated compute resources for Fabric workloads with auto-scaling capabilities
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-**[Event Hub](https://learn.microsoft.com/en-us/azure/event-hubs/event-hubs-about)** - High-throughput streaming service for real-time data ingestion and event processing
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**Phase 2** manages Fabric components using Python scripts with intelligent resource detection:
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-**Workspace** - Collaborative environment hosting all Fabric artifacts and configurations
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-**Eventhouse & KQL Database** - Real-time analytics engine with high-performance query capabilities and pre-configured schema
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-**Eventstream & Connections** - Data pipeline orchestration connecting Event Hub to Eventhouse with transformation rules
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-**Real-Time Dashboard** - Interactive monitoring interface with live visualizations and drill-down analytics
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-**Activator Rules** - Automated anomaly detection system with configurable alert thresholds and notifications
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-**Sample Data** - Pre-loaded telemetry data for immediate testing and demonstration purposes
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-**[Workspace](https://learn.microsoft.com/fabric/fundamentals/workspaces)** - Collaborative environment hosting all Fabric artifacts and configurations
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-**[Eventhouse](https://learn.microsoft.com/fabric/real-time-intelligence/eventhouse) & [KQL Database](https://learn.microsoft.com/en-us/fabric/real-time-intelligence/create-database)** - Real-time analytics engine with high-performance query capabilities and pre-configured schema
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-**Sample Data** - Pre-loaded telemetry data for immediate testing and demonstration purposes
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-**[Eventhub connection](https://learn.microsoft.com/fabric/real-time-intelligence/event-streams/add-source-azure-event-hubs?pivots=basic-features)** - Connection to the deployed Event Hub resource in Azure
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-**[Eventstream](https://learn.microsoft.com/en-us/fabric/real-time-intelligence/event-streams/overview?tabs=enhancedcapabilities)** - Data pipeline orchestration connecting Event Hub to Eventhouse with transformation rules
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-**[Real-Time Dashboard](https://learn.microsoft.com/en-us/fabric/real-time-intelligence/dashboard-real-time-create?tabs=create-manual%2Ckql-database)** - Interactive monitoring interface with live visualizations and drill-down analytics
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-**[Activator](https://learn.microsoft.com/en-us/fabric/real-time-intelligence/data-activator/activator-introduction)** - Automated anomaly detection system with configurable alert thresholds and notifications
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-**[Data Agent](https://learn.microsoft.com/en-us/fabric/data-science/concept-data-agent)** - AI-powered conversational interface for natural language data queries with configured notebook
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-**Folder** - Organizational container for grouping items required for Data Agent set up
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-**[Environment](https://learn.microsoft.com/en-us/fabric/data-engineering/create-and-use-environment)** - Fabric Environment for managing libraries, dependencies, and compute configurations required to run the notebook that configures the Data Agent
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-**[Notebook](https://learn.microsoft.com/en-us/fabric/data-engineering/how-to-use-notebook)** - Fabric Notebook with the script that uses [Fabric data agent Python SDK (in preview)](https://learn.microsoft.com/en-us/fabric/data-science/fabric-data-agent-sdk) to configure the Data Agent
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The entire process is orchestrated by Azure Developer CLI with comprehensive error handling and rollback capabilities.
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@@ -286,6 +295,10 @@ azd env set FABRIC_EVENT_HUB_CONNECTION_NAME "my_eventhub_connection"
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azd env set FABRIC_RTIDASHBOARD_NAME "My Custom Dashboard"
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azd env set FABRIC_EVENTSTREAM_NAME "my_custom_eventstream"
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azd env set FABRIC_ACTIVATOR_NAME "my_custom_activator"
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azd env set FABRIC_DATA_AGENT_NAME "my_custom_dataagent"
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azd env set FABRIC_DATA_AGENT_CONFIGURATION_FOLDER_NAME "my_custom_folder"
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azd env set FABRIC_DATA_AGENT_CONFIGURATION_ENVIRONMENT_NAME "my_custom_environment"
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azd env set FABRIC_DATA_AGENT_CONFIGURATION_NOTEBOOK_NAME "my_custom_notebook"
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```
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**Alert Configuration:**
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|`FABRIC_EVENTSTREAM_NAME`| Eventstream name |`rti_eventstream_<env-name><suffix>`|
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|`FABRIC_ACTIVATOR_NAME`| Activator name |`rti_activator_<env-name><suffix>`|
|`FABRIC_DATA_AGENT_NAME`| Data Agent name |`rti_dataagent_<env-name><suffix>`|
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|`FABRIC_DATA_AGENT_CONFIGURATION_FOLDER_NAME`| Folder name for organizing data agent configuration components |`rti_dataagentconfig_<env-name><suffix>`|
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|`FABRIC_DATA_AGENT_CONFIGURATION_ENVIRONMENT_NAME`| Environment name with the python libraries required to configure data agent |`rti_environment_<env-name><suffix>`|
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|`FABRIC_DATA_AGENT_CONFIGURATION_NOTEBOOK_NAME`| Notebook to set up the Data Agent configuration |`rti_notebook_<env-name><suffix>`|
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#### System-Managed Variables
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@@ -395,6 +412,10 @@ azd env set FABRIC_EVENT_HUB_CONNECTION_NAME "my_eventhub_connection"
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azd env set FABRIC_RTIDASHBOARD_NAME "My Custom Dashboard"
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azd env set FABRIC_EVENTSTREAM_NAME "my_custom_eventstream"
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azd env set FABRIC_ACTIVATOR_NAME "my_custom_activator"
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azd env set FABRIC_DATA_AGENT_NAME "my_custom_dataagent"
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azd env set FABRIC_DATA_AGENT_CONFIGURATION_FOLDER_NAME "my_custom_folder"
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azd env set FABRIC_DATA_AGENT_CONFIGURATION_ENVIRONMENT_NAME "my_custom_environment"
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azd env set FABRIC_DATA_AGENT_CONFIGURATION_NOTEBOOK_NAME "my_custom_notebook"
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```
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> **Note:** These are optional. If not set, defaults will use your environment name and a generated suffix.
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7. Data Agent configuration through Runbook (Preview)
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> **Preview Feature Notice:** Steps involving Fabric Data Agent configuration are [Preview features](https://learn.microsoft.com/en-us/fabric/data-science/fabric-data-agent-sdk). If these steps fail, the core functionality will still work, and you can complete the setup manually using our guides.
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### 4.6 Verify Deployment Success
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⚠️ **Deployment Issues?** Check [Known Issues and Troubleshooting](#known-issues-and-troubleshooting) for common solutions.
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> **Preview Feature Notice:** If the Data Agent setup fails during deployment (step 14), the core Real-Time Intelligence functionality will still work. You can complete the Data Agent setup manually using the [Fabric Data Agent Guide](./FabricDataAgentGuide.md).
- Sample data pre-loaded for testing and demonstration
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- Real-time dashboards for operational monitoring
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- Automated alerting with Activator for anomaly detection
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- Eventstream for data pipeline orchestration
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- AI-powered Data Agent for conversational queries
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- Fabric runbook and environment for Data Agent configuration
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---
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## Step 5: Post-Deployment Configuration
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### 5.1 Set Up Fabric Data Agent
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### 5.1 Verify Fabric Data Agent Setup
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Enable the AI-powered Fabric Data Agent to answer natural language questions about your data.
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The AI-powered Fabric Data Agent is automatically configured during deployment to answer natural language questions about your data.
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-**Setup and Configuration:** See [Fabric Data Agent Guide](./FabricDataAgentGuide.md)
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-**Verification and Usage:** See [Fabric Data Agent Guide](./FabricDataAgentGuide.md)
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-**Demonstration Flow:** See [Demonstrator's Guide - Step 1](./DemonstratorGuide.md#step-1-demonstrate-the-fabric-data-agent)
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> **Note:** Data Agent automation is a Preview feature. If the automated setup fails during deployment, you can complete the setup manually using the guide above.
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### 5.2 Start Event Simulation
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Test your new environment with real-time streaming data.
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- Eventhouse (real-time analytics engine)
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- KQL Database (high-performance queries)
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- Sample data (pre-loaded for testing)
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- Real-time Dashboards (operational monitoring)
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- Eventstream (data pipeline orchestration)
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- Activator (anomaly detection and alerting)
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- Sample data (pre-loaded for testing)
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- Data Agent (AI-powered conversational queries)
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- Folder (organizational container for Data Agent configuration components)
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- Notebook (Data Agent configuration (Preview))
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- Environment (library and compute management)
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#### Data Agent
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AI-powered conversational data interface:
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-**Name:**`rti_dataagent_<env-name><suffix>`
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-**Type:** Fabric Data Agent for natural language queries
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-**Connected Database:** KQL Database for real-time analytics
-**Type:** Fabric Environment for managing Python libraries and dependencies
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-**Configuration:** Pre-configured with required code to set up Data Agent
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-**Purpose:** Provides automation to set up configuration of the Data Agent
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> **Note:** Data Agent configuration script is a Preview feature and may have limitations. If automated setup fails, refer to the [Fabric Data Agent Guide](./FabricDataAgentGuide.md) for manual configuration.
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