Aggie BI Learning Path
A structured journey from "what is Fabric?" to "I automated 20 reports with a script." Each level builds on the previous one. Start where your experience level fits — every guide is designed to work standalone, but they're better together.
Audience
Everyone — from first-time report builders to senior data engineers
Time Investment
Getting Started: 1 hour · Beginner: 1 day · Intermediate: 1 week · Expert: ongoing
Prerequisites
A TAMU NetID and an A5 license (all employees have this)
You Already Have Power BI Pro
Every TAMU employee with an A5 license already has Power BI Pro included — no additional license purchase needed. You can view, create, and share reports immediately. This is true at every Fabric capacity tier from F2 to F2048 — the F64 "free viewer" threshold is irrelevant for internal users.
Level 0: Getting Started
Time: ~1 hour. Goal: tools installed, workspace access, first connection.
| Guide | What You'll Learn | Status |
|---|---|---|
| Install Your Tools | Power BI Desktop, VS Code, Git, TAMU VPN requirements | Planned |
| Request Workspace Access | Who to contact, what Entra groups to join, how roles work | Planned |
| Navigate the Workspace | OneLake, Lakehouse, semantic models, reports — where everything lives | Planned |
| Glossary | Every acronym and Fabric term in one page — TMDL, PBIR, DirectLake, CU, OneLake, medallion, Delta | Planned |
What You'll Have After Level 0
- Power BI Desktop installed and connected to a Fabric workspace
- VS Code with Git ready for code-first development (later)
- Understanding of the workspace structure and where data lives
- A vocabulary to understand the rest of the documentation
Level 1: Beginner
Time: ~1 day. Goal: build, theme, and publish your first report.
| Guide | What You'll Learn | Status |
|---|---|---|
| Build Your First Report | Connect to the Lakehouse → build a bar chart → add a slicer → see your data | Planned |
| Apply the Aggie Brand Theme | Load the theme JSON, understand what it changes, verify contrast | Planned |
| Understand the Date Dimension | Fiscal year (Sept 1), date hierarchies, time intelligence basics | Planned |
| Publish to the Power BI App | Publish from Desktop → configure the App → share with your team | Planned |
| Troubleshooting: Common First Errors | "Can't connect," "refresh failed," "no data showing" — the top 10 with fixes | Planned |
What You'll Have After Level 1
- A working report with the Aggie Brand Theme, connected to real data
- Published to a Power BI App that your team can access
- Ability to troubleshoot the most common issues independently
Level 2: Intermediate
Time: ~1 week. Goal: build production-quality reports with proper data modeling and security.
| Guide | What You'll Learn | Status |
|---|---|---|
| DAX Patterns for AggieDB | Common measures (YTD, MoM, % change), fiscal year calcs, SWITCH for parameters, CALCULATE fundamentals | Planned |
| Power Query Best Practices | Query folding, the AccessToken pattern, why literal URLs matter, pagination | Power Query Reference |
| Data Modeling with AggieDB | Star schema, conformed dimensions, relationship cardinality, why bidirectional filters are dangerous | Planned |
| Row-Level Security (RLS) Step-by-Step | Build RLS from scratch — security table, DAX role, USERPRINCIPALNAME(), testing, deployment | Planned |
| DirectLake vs Import vs DirectQuery | When to use each, performance tradeoffs, automatic fallback behavior, how to check which mode is active | Planned |
| Report Distribution & App Management | Power BI App setup, audience targeting, update frequency, sharing vs apps, "Publish to Web" risks | Planned |
What You'll Have After Level 2
- Reports with proper DAX measures (not card visuals with raw columns)
- Star schema data models following AggieDB conventions
- Row-level security protecting sensitive data
- Reports distributed via Power BI Apps with proper audience targeting
Level 3: Expert
Time: ongoing. Goal: architect and automate the full data platform.
| Guide | What You'll Learn | Status |
|---|---|---|
| Code-First Development: Why & How | Why we version-control JSON instead of clicking — the motivation, workflow, and what changes | Planned |
| TMDL & PBIR (via Code-First) | Reading and editing semantic model definitions and report layouts in text — the "view source" of Power BI | Planned |
| CI/CD Pipeline Setup | VNet-injected runners, OIDC auth, deployment scripts | CI/CD Security |
| Git Sync Workflow | What syncs, conflict resolution, service principals | Git Sync |
| Gateway Architecture & Sizing | The shared gateway bottleneck, sizing, Mirroring strategy | Gateways |
| Performance Optimization | Query folding, DAX query plans, visual reduction, DirectLake optimization, "hide unused columns" trick | Planned |
| Composite Models & Cross-Workspace Queries | When you need data from multiple workspaces in one report — shortcuts, chained models, tradeoffs | Planned |
| Capacity Engineering | CU smoothing, spike diagnosis, refresh scheduling, cost attribution | Capacity Planning |
What You'll Have After Level 3
- Ability to build and deploy reports entirely through Git and CI/CD
- Deep understanding of capacity consumption and optimization
- Architecture skills for multi-workspace, multi-source deployments
- Gateway infrastructure that doesn't bottleneck
Level 4: Mastery of Aggie BI
Time: mastery is a practice, not a destination. Goal: define and extend the platform itself.
| Guide | What You'll Learn | Status |
|---|---|---|
| Automated Report Generation (AggieViz) | The scripting system that generates 20 reports, 48 pages, 1,149 visuals from code | AggieViz |
| Shell Visual System | Z-order schemes, canvas layout, theme-responsive visuals | Shell Visual |
| PBIP Property Reference | JSON property reference for every visual type — the API documentation for Power BI layouts | PBIP Reference |
| Extending AggieDB (via Contributing) | Adding new domains, new conformed dimensions, proposing schema changes through ADR | Planned |
| Copilot for AggieDB | Using Fabric Copilot effectively with standardized data — NL queries, DAX generation, notebook assistance | Planned |
| Multi-Group Architecture | Designing workspace topology for multiple departments sharing a platform | Multi-Group |
| Database Decision Matrix | Lakehouse vs Warehouse vs SQL DB vs Cosmos DB — architectural decisions | Decision Matrix |
| Contributing to the Standard | How to propose changes to AggieDB, the brand theme, or the AggieViz system — ADR process, testing, review | Planned |
What You'll Have After Level 4
- Ability to extend the platform itself — new data domains, new report patterns, new automation
- Deep understanding of the architectural decisions behind every layer
- Skills to mentor others through Levels 1–3
- Confidence to represent TAMU's analytics architecture to Microsoft, vendors, and peer institutions
Quick Reference: "I Need To..."
| I need to... | Start here |
|---|---|
| See a report someone shared with me | Level 0: Navigate the Workspace |
| Build my first report | Level 1: Build Your First Report |
| Add security so people only see their department | Level 2: RLS Step-by-Step |
| Understand why my refresh is slow | Level 3: Performance Optimization |
| Set up CI/CD for my team's reports | Level 3: CI/CD Pipeline |
| Automate report generation from a script | Level 4: AggieViz |
| Choose between Lakehouse and Warehouse | Level 4: Database Decision Matrix |
| Fix a broken refresh / gateway error | Level 1: Troubleshooting |
| Understand an acronym | Level 0: Glossary |
Content Status
This learning path is a living document. Guides marked Planned are in development. Guides with links are published and available now. The existing Fabric, AggieViz, AggieDB, and M365 Reporting documentation is comprehensive — the planned guides fill gaps in the beginner-to-intermediate journey and add explicit tutorials.
Platform concepts, DirectLake, governance
Naming conventions, schema design, conformed dimensions
The five-layer stack from data to governance