Fabric Shortcut Inventory
A Microsoft Fabric notebook that scans workspaces and builds a complete inventory of OneLake shortcuts, detecting orphaned, circular and ungoverned external shortcuts, with an interactive HTML report and optional persistence to a Delta table. Why? OneLake shortcuts are easy to create and hard to govern: over time you accumulate shortcuts pointing to deleted items, circular chains between lakehouses, and connections to external storage (S3, ADLS, GCS…) with no sensitivity label or endorsement whatsoever. Fabric offers no centralized view of any of this. This notebook builds one in a single run. What it does Discovers workspaces — the entire tenant (with admin permissions) or a specific list, by name or ID. Discovers items — lists the items in each workspace via the Fabric REST API. Extracts shortcuts — in parallel, from the item types that support them (Lakehouse, KQL Database, Mirrored Database). Parses targets — normalizes OneLake targets (workspace/item/path) and external ones (Amazon S3, ADLS Gen2, Google Cloud Storage, S3 compatible, Dataverse, Azure Blob Storage, OneDrive/SharePoint). Validates: Orphaned: the target item no longer exists within the scanned scope. Circular: shortcut chains that form a cycle (DFS detection over the dependency graph). Governance: external shortcuts on items with no sensitivity label and no endorsement (Certified/Promoted). Resolves names — turns target GUIDs into human-readable names, even for targets outside the scanned scope. Presents — an interactive HTML report with 4 views (Overview, Circular, Orphan, Governance) and per-row color coding. Persists (optional) — writes the inventory to a Delta table so you can query it with SQL or build Power BI reports on top All the code and documentation can be found in the Github Repository. https%3A%2F%2Fgithub.com%2Fkilianbs%2Ffabric-shortcut-inventory%2Fblob%2Fmain%2Fshortcut_inventory_en.ipynbSemantic Link - Semantic Radar
This solution presents an automated governance framework for Microsoft Fabric semantic models that helps organizations detect duplication, reduce semantic model sprawl, and improve reuse across workspaces and capacities. The notebook scans semantic models using either service principal or current-user access, analyzes datasource and metadata patterns, identifies duplicate or overlapping models, and turns the findings into actionable governance outputs for administrators and data owners. Capabilities: Semantic Model Discovery: Scans semantic models across Microsoft Fabric workspaces and capacities, with support for admin APIs, workspace-level access, and optional workspace targeting. Duplicate Source Detection: Identifies semantic models that connect to the same normalized datasource and highlights consolidation opportunities to promote shared semantic layers and reduce redundant logic. Probable Overlap Detection: Detects likely duplicate or overlapping semantic models by comparing entities, columns, and metadata, even when datasource metadata is incomplete. Actionable Governance Outputs: Produces prioritized action plans, executive summaries, responsibility views, diagnostics, and compact visual tables that help teams focus on the most important governance decisions. Ownership and Collaboration Support: Maps impacted semantic models to dataset owners and workspace admins, and generates email drafts to accelerate review, coordination, and remediation. Noise Reduction and Flexibility: Supports exclusions for intentional duplicates, sandbox models, and workspace-specific exceptions, while suppressing likely deployment clones to reduce false positives. Microsoft Fabric-Native Experience: Designed as a Fabric notebook solution with visual-first output, configurable execution modes, and optional SemPy enrichment for deeper semantic model metadata. Business value: This solution helps data teams improve governance, reduce duplication, strengthen semantic model reuse, and create a clearer path toward centralized, maintainable, and trusted analytics assets in Microsoft Fabric. All code and documentation can be found in the GitHub repository. https%3A%2F%2Fgithub.com%2Fchristiantodte%2FSemanticRadarSemantic Model Version Control & Change Diff
Power BI's built-in version history is UI-only — capped at 5 snapshots,14-day retention, and zero API access. Enterprise teams have no programmatic way to list, compare, or audit model changes.This notebook bridges that gap using Semantic Link (SemPy) and Microsoft Fabric. It captures point-in-time snapshots of all five semantic model artifact types — Measures, Columns, Relationships, Tables, and RLS Roles — and persists them as Delta tables in OneLake via Apache Spark. A field-level diff engine then compares any two run IDs and classifies every change as ADDED, REMOVED, or MODIFIED with before/after values. Key capabilities: Dual-path extraction: fabric.list_*() API with automatic DMV fallback. Unlimited history beyond the built-in 5-version / 14-day limit. Field-level diff across all artifact types including DAX expressions. RLS role and membership change detection for security auditing. Interactive ipywidgets UI with Fabric-branded HTML output. Delta append mode — queryable directly from Power BI reports. Schedulable via Fabric Data Pipeline for automated daily governance Built entirely within Microsoft Fabric using sempy.fabric, sempy_labs, PySpark, Delta Lake, and ipywidgets. No external tools or services required. Github-https://github.com/praful-potphode/Fabric-Semantic-Link-Competition-2026 LinkedIn-https://www.linkedin.com/in/praful-p-912349241/ Community Profile-https://community.fabric.microsoft.com/t5/user/viewprofilepage/user-id/1261729 https%3A%2F%2Fgithub.com%2Fpraful-potphode%2FFabric-Semantic-Link-Competition-2026Semantic Link – Semantic Radar
This solution presents an automated governance framework for Microsoft Fabric semantic models that helps organizations detect duplication, reduce semantic model sprawl, and improve reuse across workspaces and capacities. The notebook scans semantic models using either service principal or current-user access, analyzes datasource and metadata patterns, identifies duplicate or overlapping models, and turns the findings into actionable governance outputs for administrators and data owners. Capabilities: Semantic Model Discovery: Scans semantic models across Microsoft Fabric workspaces and capacities, with support for admin APIs, workspace-level access, and optional workspace targeting. Duplicate Source Detection: Identifies semantic models that connect to the same normalized datasource and highlights consolidation opportunities to promote shared semantic layers and reduce redundant logic. Probable Overlap Detection: Detects likely duplicate or overlapping semantic models by comparing entities, columns, and metadata, even when datasource metadata is incomplete. Actionable Governance Outputs: Produces prioritized action plans, executive summaries, responsibility views, diagnostics, and compact visual tables that help teams focus on the most important governance decisions. Ownership and Collaboration Support: Maps impacted semantic models to dataset owners and workspace admins, and generates email drafts to accelerate review, coordination, and remediation. Noise Reduction and Flexibility: Supports exclusions for intentional duplicates, sandbox models, and workspace-specific exceptions, while suppressing likely deployment clones to reduce false positives. Microsoft Fabric-Native Experience: Designed as a Fabric notebook solution with visual-first output, configurable execution modes, and optional SemPy enrichment for deeper semantic model metadata. Business value: This solution helps data teams improve governance, reduce duplication, strengthen semantic model reuse, and create a clearer path toward centralized, maintainable, and trusted analytics assets in Microsoft Fabric. All code and documentation can be found in the GitHub repository https%3A%2F%2Fgithub.com%2Fchristiantodte%2FSemanticRadarSemantic Link – DAXLoadTest
Every semantic-model developer asks the same question before a deploy: "will this regress under real user load?" Today there's no answer, Power BI teams either hand-write synthetic DAX or skip load-testing entirely. DAXLoadTest replays the real DAX queries your users have been running against your semantic model, under configurable concurrency, and pinpoints which queries slow down — then tells you the exact Report → Page → Visualresponsible for each regression. The tool combines three Fabric capabilities that have never been stitched together before: Workspace Monitoring Eventhouse as the query corpus, the only source of historical DAX text in Fabric sempy.fabric.evaluate_dax for parallel replay, with a novel DAX-VAR-literal tagging trick that survives XMLA normalization and gives a bulletproof join key between sent queries and trace logs sempy_labs.report.ReportWrapper to auto-map VisualId GUIDs to human-readable report/page/visual names Results land in an interactive Plotly dashboard (KPI tiles, regression bar chart, baseline-vs-load scatter, drill-down table) rendered inline. No Lakehouse, no Delta tables, no setup, set 4 parameters, run the notebook, read the dashboard. Drop it into a Data Factory pipeline to catch performance regressions before they reach users. External libraries used: semantic-link-labs (sempy_labs) — Microsoft's extension library. Used for sempy_labs.report.ReportWrapper.list_visuals() to enrich queries with Report → Page → Visual metadata. Repo: github.com/microsoft/semantic-link-labs plotly — interactive dashboard rendering (KPI indicators, bar chart, log-log scatter, drill-down table). pandas / numpy — in-memory analytics and join orchestration (pre-installed). PySpark + Azure Data Explorer Spark connector (com.microsoft.kusto.spark.synapse.datasource) — pre-installed on Fabric; used to query the Workspace Monitoring Eventhouse via KQL. Github Repo Link: Semantic Link - DAXLoadTest https%3A%2F%2Fgithub.com%2Ftharunkumarr98%2FsemanticLinkChallenge2026%2Fblob%2Fmain%2F2026_SemanticLink_tharunkumarr98_DAXLoadTest.ipynb
Semantic Link - Semantic Model Chaos Lab
# Semantic Link - Semantic Model Chaos Lab Semantic Model Chaos Lab brings chaos engineering to Fabric semantic models. It uses Semantic Link to create a lightweight digital twin of a selected semantic model: metadata, measures, relationships, roles, Copilot readiness signals, and baseline DAX checks. The notebook then simulates realistic schema, relationship, DAX, metadata, and security changes against that model twin to measure blast radius before deployment. The generated release gate report shows broken measures, value drift, impacted model objects, security role risk, Copilot readiness degradation, and suggested remediation. The result is a reusable developer tool that helps BI teams prevent semantic model incidents before they reach production. Note: I realize the official submission deadline has passed, but I wanted to share this as a late community showcase because the idea explores a different angle for Semantic Link: model twins, chaos engineering, and release gates for semantic models. https%3A%2F%2Fgithub.com%2Fflthibau%2Fsemantic-model-chaos-lab%2Fblob%2Fmain%2F2026_SemanticLink_flthibau_SemanticModelChaosLab.ipynbSemantic Link – MetaData Extractor
📘 Notebook Description The notebook is designed to extract, enrich, and visualize semantic model metadata and measure-level dependencies within Microsoft Fabric. It connects to a specified semantic model, retrieves tables, columns, measures, and relationships, and enhances measures with AI-generated business descriptions. The notebook then derives measure-to-measure dependencies from DAX expressions, builds a Directed Acyclic Graph (DAG), and presents both tabular lineage outputs and graph-based visualizations. This enables a deep understanding of how calculations are structured and interrelated within the semantic model. 🌟 Benefits Measure Lineage Transparency: Provides clear visibility into how measures are built on top of one another, improving model understanding and governance. AI-Powered Documentation: Automatically generates business-friendly descriptions for DAX measures, reducing manual documentation effort. Impact Analysis: Identifies upstream and downstream dependencies between measures, helping assess the impact of logic changes GitHub URL : https://github.com/NandanHegde15/MSFTFabric-Notebook/blob/main/SemanticModel/Metadata%20Extraction/2026_SemanticLink_NandanHegde_MetadataExtractor.ipynb https%3A%2F%2Fgithub.com%2FNandanHegde15%2FMSFTFabric-Notebook%2Fblob%2Fmain%2FSemanticModel%2FMetadata%2520Extraction%2F2026_SemanticLink_NandanHegde_MetadataExtractor.ipynbSemanticLink_SemanticModelGeneration
This notebook explores the New York Taxi for hire vehicle data as part of the **Fabric Semantic Link Developer Experience Challenge**. It shows an end-2-end example of using the semantic link package for automating the semantic model generation. This notebook and development is based upon the following problem and solution. ### Problem - Semantic model changes are often manual, UI‑driven, and are hard to track - Business logic is duplicated across models and environments - Advanced features like calculation groups are costly to reimplement repeatedly - Limited transparency and reusability in traditional development workflows ### Solution - Use Semantic Link + TOM to manage semantic models programmatically, based on metadata - Centralize all logic in a single, auditable notebook - Automate repetitive tasks such as calculation groups and standard measures, based on datatypes - Integrate seamlessly with DevOps for versioning, review, and deployment of the notebook - Make fully use of the metadata of the tables https://github.com/bbreugel/CommunityNotebooks/blob/main/2026_SemanticLink_benitovbreugel_ModelGeneration.ipynb https%3A%2F%2Fgithub.com%2Fbbreugel%2FCommunityNotebooks%2Fblob%2Fmain%2F2026_SemanticLink_benitovbreugel_ModelGeneration.ipynbSemantic Link - Model Health & Security Suite
One notebook. 13 tools. Zero guesswork. A comprehensive diagnostic, remediation, and security auditing toolkit for Microsoft Fabric semantic models — delivered as a single self-contained Fabric notebook with an interactive ipywidgets console. Scans any Import / Direct Lake / DirectQuery model, produces a full HTML dashboard with scores and findings, and auto-applies fixes via TOM writes. Three features in this notebook solve problems that no existing Fabric or Power BI tool addresses end-to-end: 🔒 Security X-Ray — Effective Access Map Enterprise Power BI security is managed through Azure AD groups, not individual users. An AD group assigned to an RLS role might contain 200 users, nested sub-groups, and service principals. Until now there was no tool, no REST API, no sempy function that answered: "Given all the RLS filters, OLS restrictions, AD group memberships, and workspace roles — what does each real human actually see?" Security X-Ray combines TOM + DAX INFO.ROLES() + Microsoft Graph /transitiveMembers + Fabric REST roleAssignments into a single Effective Access Map that flags the critical blind spot: users who bypass RLS via workspace Admin/Member/Contributor privileges — a silent risk the standard Power BI UI never surfaces. 🤖 AI Generation — Schema-Aware Descriptions at Scale Writing meaningful descriptions, synonyms, and AI instructions across hundreds of tables, columns, and measures is impractical by hand. Generic AI prompts miss business context and produce bland, interchangeable text that weakens Copilot quality. Bringing rich schema context into AI generation from inside a Fabric notebook hasn't been possible — until now. "How do I make every measure, column, and table in my model Copilot-ready with descriptions, synonyms, and AI instructions that actually reflect my business — without writing them all by hand?" AI Generation combines Azure OpenAI via Fabric Copilot auth (no API key, no endpoint config — just synapse.ml.fabric.credentials.get_openai_httpx_sync_client()) with full schema context injection: relationships, data types, DAX expressions, and user-supplied business domain. Every generated description is grounded in the actual model, not hallucinated. Reviewable plan before any TOM write, with graceful fallback to rule-based templates when AI is unavailable. 🔄 Model Diff — Cross-Workspace Drift Detection Semantic models live across multiple workspaces — dev, test, UAT, prod, client A, client B. There is no built-in Fabric tool that compares two models to answer "what changed?" Drift happens silently: someone adds a measure in dev, tweaks a DAX formula in prod, removes a column during a refactor — and these changes go unnoticed until a report breaks. "What changed between my dev model and my prod model? Which tables, columns, or measures were added, removed, or modified — and are any DAX expressions different?" Model Diff uses sempy TOM reads across two workspaces + tabular schema comparison + DAX expression normalization to produce a structured drift report covering tables, columns, measures, relationships, and DAX changes. Essential for DataOps / release workflows and multi-tenant engagements where models live in many places. 🎯 13 Tools In One Scan Copilot Readiness — descriptions, synonyms, hidden keys, format strings, AI Prep Lineage — measure → source, blast radius, unused columns, dead code Test Framework — every DAX measure evaluated, relationships validated Model Diff — compare across workspaces (dev vs prod drift) Report Visuals — broken refs, unused measures (model-filtered) Security Audit — role compliance, RLS/OLS coverage Security X-Ray — effective access + workspace bypass detection Data Quality — referential integrity, null-rate (batched DAX UNION, 10× faster) DAX Dependencies — fan-in/fan-out, circular references sempy_labs integrations — BPA, Vertipaq, Direct Lake, Capacity (opt-in) ⚙️ Auto-Fix 9 Categories Via TOM Preview all generated fixes in a reviewable plan, then apply selected ones: AI/template descriptions, synonyms (Culture ObjectTranslation), hidden keys, format strings, summarize-by, model CustomInstructions, AI instructions, schema reduction (isAvailableInMDX + LinguisticMetadata Entity Visibility), role descriptions. 🏗️ Technical Highlights Chunked HTML rendering — overcomes Fabric's ~100 KB displayHTML limit SPN Graph token via MSAL — 0.3s vs 60s+ notebookutils.credentials hang Azure OpenAI via Fabric auth — no API key needed AAD group expansion — recursive /transitiveMembers with pagination Scan log capture — contextlib.redirect_stdout keeps the config form visible Demo Mode — sequential pseudonyms for safe public screenshots 🚀 Quick Start Cell 1 (install) → Cell 2 (engine) → Cell 3 (console + Run Scan) → Cell 4 (scan log) → Cell 5 (dashboard) → Cell 6 (apply fixes). Graceful degradation: works without SPN (skips Graph expansion), without Copilot (falls back to template descriptions), without Directory.Read.All (shows object GUIDs), and read-only mode if no write access. 📦 Complete Repo GitHub — full repo, 37 screenshots, sample HTML dashboard, MIT license Author: Natarajan Manivasagan LinkedIn · Fabric Community Profile https%3A%2F%2Fgithub.com%2FNatarajanManivasagan%2FFabric-Semantic-Link-Developer-Experience-Challenge-2026-Apr%2Fblob%2Fmain%2F2026_SemanticLink_NatarajanManivasagan_ModelHealthAndSecuritySuite.ipynbSemantic Link – Moodle LMS Analytics & Model Governance Toolkit
This notebook delivers a reusable Moodle LMS analytics and governance toolkit built entirely in Microsoft Fabric using Semantic Link Labs. It connects a real Moodle MySQL database through a Bronze-Silver Medallion Architecture, creates a Direct Lake semantic model with 31 measures across 8 folders, and uses sempy_labs for BPA, Vertipaq analysis, Hindi translation, and automated model refresh. Unique developer tools include DAX unit testing, DAX vs Silver data validation, model snapshot & diff, and custom Moodle-specific BPA rules. Link to GitHub URL- MoodleLMSToolkit https%3A%2F%2Fgithub.com%2Fcseprs%2FMoodleLMSToolkit
