r/PowerBI

Hi everyone,

I've just finished the first version of a Power BI dashboard built on the Wikimedia Pageviews dataset.

The goal was to explore what people around the world are reading on Wikipedia.
I'd love some feedback from experienced Power BI users, especially on:

• Dashboard layout and navigation
• Choice of visuals
• Readability and overall user experience
• Any Power BI best practices I'm missing

You can explore the dashboard here:
Dashboard: Dashboard Link

If you're curious about the data pipeline behind it, the project is open source:
GitHub: Github Link

Data source: Wikimedia Pageviews (via BigQuery Public Datasets), Wikipedia Public API
Visualization: Power BI

Thanks in advance for any suggestions!

Hey everyone,

I recently built this Executive Retail Dashboard in Power BI as part of my portfolio.

It includes:

• KPI cards (Total Sales, Total Profit, Total Orders, Profit Margin)
• Interactive filters (Year, Month, Region, Category)
• Sales trend analysis
• Regional and category performance
• Top-performing products
• Drill-through pages for deeper analysis
• Custom tooltips for additional insights
• Page navigation buttons for a smoother user experience
• Clean executive dark theme

I built it to be reusable, so it can be connected to different datasets with only a few changes.

I'm looking for honest feedback on the dashboard design, layout, navigation, KPIs, and overall user experience.

What would you improve or add in Version 2?

Thanks for taking the time to check it out. I appreciate any feedback or suggestions!

Hey everyone,

If you've worked with Microsoft Fabric medallion architectures, you know the pain: a column gets renamed or dropped in Bronze or Silver, and a few days later, a business user reports that three Power BI dashboards are showing blanks. Tracking down what broke across the workspace can be a nightmare.

To solve this, I built Microsoft Fabric Schema Drift Detection— an autonomous system that detects schema drift across the entire medallion architecture, maps it using a column-level lineage graph, alerts the team, and drafts auto-fix pull requests.

Here is the GitHub repository if you want to check it out: https://github.com/naveenjujaray/microsoft-fabric-schema-drift-detection

🕵️ How It Works

Instead of just checking if a table schema changed, this tool maps cross-layer lineage all the way to reports:

>Bronze (Lakehouse) ➔ Silver (Lakehouse) ➔ Gold (Warehouse) ➔ Semantic Model ➔ Power BI Reports (PBIP)

1. Cross-Layer Lineage: If \silver.customers.email` is renamed to `email_address`, the tool walks the lineage graph to detect that it will break `gold.Dim_Customer.Email`, which breaks the semantic model `Customer.Email`, which ultimately breaks the visual in your "Customer Detail" Power BI report.`

2. DAX Parsing: It parses DAX measures (like \SUM(Sales[Freight])`) to flag downstream breaks if upstream columns are dropped or modified.`

3. AI Impact Analysis: An optional Claude-powered agent reviews the drift, judges the business severity (e.g., info vs. critical), and drafts the Pull Request description.

4. Auto-Fix PRs: The tool automatically drafts a new Git branch and applies find/replace TMDL fixes to your Power BI Project (PBIP) files in Git, opening a PR to fix the broken references automatically.

5. Multi-Channel Alerts: Sends rich, native notifications to MS Teams (Adaptive Cards), Slack (Block Kit), and Outlook (HTML emails).

🏛️ Running It (Simulate Mode)

One of my main goals was to make this easy to test without needing a live Fabric tenant or incurring capacity costs. The tool includes a **simulate mode** that spins up a local DuckDB replica of AdventureWorksLT to mock the medallion layers:

# Clone the repository
git clone https://github.com/naveenjujaray/microsoft-fabric-schema-drift-detection.git
cd microsoft-fabric-schema-drift-detection

# Install requirements
pip install -r requirements.txt

# Run the 60-second demo simulation
python main.py --mode simulate --once --dry-run

This will snapshot baselines, inject 5 kinds of schema drift, and print out the entire detection report, the PR it would open, and all the Teams/Slack/Outlook payloads.

🏭 Running inside Fabric

It also fully integrates into a real Fabric tenant. You can run the code inside a Fabric Notebook (using the built-in notebook identity for auth) and schedule it as a Data Pipeline that fails on critical drift to serve as a CI gate.

Would love to hear your thoughts, feedback, or any features you'd like to see added!

For a long time, I’ve felt that while Power BI has evolved, many teams are still stuck treating their semantic models like advanced Excel files. Manual deployments are no longer just "slow", they’ve become a hard bottleneck to scalability.

I’ve decided to document my own personal project over the next few weeks: a complete, zero-cost Enterprise Power BI DevOps blueprint. My goal is to apply code-first software engineering principles to Microsoft Fabric and Power BI.

The architectural shift I’m documenting:

Separation of Metadata vs. Data: Treating schema changes (measures, relationships) as instant metadata operations, completely decoupled from data processing.

Separation of Model vs. Report: Treating the core semantic model as a standalone "data product" that is versioned and deployed independently of the visual layer.

I’m starting this series now and will be posting the architecture, the GitHub Action pipelines, and the XMLA orchestration logic as I build it out.

If you're interested, I can give the link to follow along with the series, in the comments.

But my main focus is, I'm curious to hear from the community here in this subreddit since I've read so many different topics on Power BI for years in this subreddit. I think your opinions would help my journey.

As we move into this "code-first" world for our semantic layers, what is the biggest technical hurdle you’ve faced? Is it the XMLA/Service Principal permissioning, or is it more of a cultural shift in how your teams handle PBIX version control?

EDIT: I honestly never expected this much of a response! Thank you all for the incredible discussions and for sharing your own setups. Since so many of you asked for the technical implementation, I've dropped the link to the series below as well as in the comments. Thanks folks.

here's my LinkedIn

The data is in excel format.

Hi everyone,

My name is Alex of Monster Analytics Hull UK. This is one of or our builds based on some mock data generated by AI. We are new on the market and any feedback is more then welcomed.

Cheers!