Merge pull request #53 from DAXNoobJustin/SemanticModelAudit

Add the Semantic Model Audit tool

Author: itsnotaboutthecell

tools/SemanticModelAudit/README.md

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+# Fabric Semantic Model Audit
+
+## Overview
+
+This tool is designed to provide a comprehensive audit of your Fabric semantic models. 
+
+The tool consists of three main components:
+
+1. **The Notebook:**  
+   - Captures model metadata, query logs, dependencies, unused columns, cold cache performance, and resident statistics.
+   - Generates star schema tables (DIM_ModelObject, DIM_Model, DIM_Report, DIM_User, FACT_ModelObjectQueryCount, FACT_ModelLogs, FACT_ModelObjectStatistics) stored in a lakehouse.
+   - Includes robust error handling, scheduling, and clean-up functions to support continuous monitoring.
+
+1. **The Power BI Template (PBIT File):**  
+   - Creates an interactive report from the star schema tables generated by the notebook.
+   - Allows you to explore model performance, usage trends, and metadata changes through intuitive visuals.
+   - Provides a ready-to-use template that you can customize further in Power BI Desktop.
+
+1. **The PowerPoint File:**  
+   - Contains the background images and design elements used in the Power BI template.
+
+## Requirements
+
+1. **Workspace Monitoring:**  
+   - Ensure that Workspace Monitoring is enabled in your Fabric environment.  
+   - Refer to [this blog post](https://blog.fabric.microsoft.com/blog/announcing-public-preview-of-workspace-monitoring) for setup guidance.
+
+1. **Scheduled Execution:**  
+   - Schedule the notebook to run several times a day (e.g., six times) for detailed historical tracking.
+   - Update run parameters (model names, workspaces, logging settings) at the top of the notebook.
+
+1. **Lakehouse Attachment:**  
+   - Attach the appropriate Lakehouse in Fabric to store logs and historical data in Delta tables.
+
+## Key Features
+
+1. **Model Object & Metadata Capture:**  
+   - Retrieves and standardizes the latest columns and measures using Semantic Link and Semantic Link Labs.
+   - Captures dependencies among model objects to get a comprehensive view of object usage.
+
+1. **Query Log Collection:**  
+   - Captures both summary query counts and detailed DAX query logs.
+
+1. **Unused Column Identification:**  
+   - Compares lakehouse and model metadata to identify unused columns in your model's source lakehouse.
+   - Removing unused columns will result in greater data compression and performance.
+
+1. **Cold Cache & Resident Statistics:**  
+   - Deploys a cloned model to measure cold cache performance.
+   - Records detailed resident statistics (e.g., memory load, sizes) for each column.
+
+1. **Star Schema Generation:**  
+   - Produces a set of star schema tables, making it easy to integrate with reporting tools.
+
+1. **Integrated Reporting Assets:**  
+   - **Power BI Template (PBIT):** Quickly generate an interactive report from the captured data.
+   - **PowerPoint File:** Provides background images and design elements used in the report.
+
+## Why Use This Tool?
+
+- **Comprehensive Auditing:**  
+  Automates the collection of historical metadata, query logs, and performance data for your Fabric semantic models.
+
+- **Actionable Insights:**  
+  Identify obsolete columns, understand query performance trends, and monitor usage patterns to optimize your models.
+
+- **Quickstart Reporting:**  
+  The provided PBIT file allows you to quickly start to analyze model's audit data logs.
+
+- **Scalability and Flexibility:**  
+  The tool is designed to support multiple models and run at scheduled intervals, making it suitable for continuous monitoring in large-scale environments.
+
+## Getting Started
+
+1. Download the notebook from GitHub and upload to a Fabric workspace.
+![sma-upload-notebook](media/sma-upload-notebook.png)
+
+1. Attach a Lakehouse that will be used to save the logs.
+![sma-attach-lakehouse](media/sma-attach-lakehouse.png)
+
+1. Update the list of models you want to audit.
+![sma-define-audit-models](media/sma-define-audit-models.png)
+
+1. Configure the rest of the settings in the config cell. There are a lot of options, so read carefully. 🙂
+![sma-configure-additional-args](media/sma-configure-additional-args.png)
+
+1. Run the notebook and collect the logs. Under the collect_model_statistics() cell, you can track along with the testing if you want to understand what is happening.
+![sma-track-run](media/sma-track-run.png)
+
+1. After the first run has finished, download the PBIT file and connect to your lakehouse.
+![sma-report](media/sma-report.png)
+
+## Troubleshooting & Tips
+
+- **Workspace Monitoring Setup:**  
+  Verify that your Fabric environment is properly configured if you experience issues with monitoring data.
+
+- **Capacity Considerations:**  
+  Cold cache performance testing requires cloning and refreshing models. This feature is only recommended for Direct Lake or Import models. Ensure these operations do not impact production workloads.
+
+- **Parameter Adjustments:**  
+  Customize parameters like `max_workers` and date range checking based on your model size and available resources.
+
+- **Run History Cleanup:**  
+  Use the built-in functions to clean up incomplete runs or force-delete historical tables if necessary, but exercise caution as this will remove past data.