📢Announcing our first Data Factory & Data Integration Contest!

Medallion to Magic: Using Data Factory & Copilot to Transform Your Data to Be AI Ready

 

On behalf of the Data Factory product team, we'd like to extend a huge thank you to everyone who participated in the first ever Medallion to Magic contest! This contest was labor intensive and we recognize all of the hard work, time, and energy you put into your entries.

Congratulations to NandanHegde and Hemanth Kotha on your WIN 🥳 The two of you set the bar for what “AI-ready” looks like in practice. Your solution nails the Medallion architecture, layers in genuine metadata-driven orchestration, and extends Copilot all the way through to consumption with a governed Power BI experience. Paired with strong documentation and a highly reusable healthcare scenario, this is the clearest, most complete embodiment of the contest goals. Please check your private messages for a note from us!

We encourage you to take a look at the winning entry: Metadata Driven Framework - Microsoft Fabric Community

We also nudge you to look at the other entries in the gallery: AI Ready Data Gallery - Microsoft Fabric Community

 

Finally, if you're looking to read up on some real-world implementation practices, please take a look at the latest blog post from Microsoft's own sharvu: Medallion to Magic — Manufacturing Intelligence Pl... - Microsoft Fabric Community

 

THANK YOU to everyone - this was another fun competition!!

________

 

This contest is now closed.

 

📅 Contest Timeline

• Contest opens: Monday, March 16
• Submissions close: Friday, May 8
• Judging period: May 11 - 14
• Winners announced: Friday, May 15

 

🎯 The Goal

 

This contest is about demonstrating a repeatable, end-to-end pattern for building AI-ready analytics in Microsoft Fabric by combining:

• Medallion data pipelines
• Metadata driven design
• Copilot assisted transformations
• Power BI semantic models prepared specifically for AI

The key idea: data and metadata are both first‑class inputs to AI — and your solution should treat them that way.

 

TL;DR:

 

We’re asking you to build a clear, end-to-end Fabric solution that uses a Medallion architecture: ingest raw data with pipelines, manage both data and metadata, transform it into Silver and Gold layers in a Fabric Data Warehouse (using a dbt job), and finish with an AI-ready Power BI semantic model. The goal is to show a repeatable pattern where well-designed pipelines and warehouse models lead to trustworthy Copilot answers in Power BI.

 

🏗️ What You’ll Build (At a High Level)

 

Teams will build an ELT‑style solution using Fabric where:

1. Raw data is ingested via Fabric Data Factory
2. Pipelines orchestrate data and metadata flows
3. Silver and Gold layers are created in a Fabric Data Warehouse
4. A dbt job is used to transform Silver → Gold
5. The final output is an AI-ready Power BI semantic model

This is not about one clever transformation - it’s about showing a pattern others could reuse.

 

✅ Architecture Expectations (Important!)

 

This challenge intentionally standardizes the Medallion architecture to remove ambiguity. The Fabric Data Warehouse is the authoritative storage layer.

 

• Silver and Gold must live in a Fabric Data Warehouse
• Silver and Gold must be schemas in the same warehouse
• dbt must target the Fabric Data Warehouse for all Silver → Gold transformations
• Lakehouses may be used for ingestion or exploration, but not for Silver or Gold

Data must follow a clear Bronze → Silver → Gold flow:

 

• Bronze: raw data from Microsoft and/or external sources, ingested using Fabric Data Factory with minimal transformation
• Silver: cleaned and standardized data written to a Silver schema in the warehouse and used as input to dbt
• Gold: curated, analytics‑ready models created via dbt, written to a Gold schema, and designed for Power BI and Copilot

Metadata is a required part of the architecture, not an afterthought.

 

• A dedicated metadata pipeline must be built in Fabric Data Factory
• Metadata must be captured across Bronze, Silver, and Gold, including tables, columns, data types, and business meaning
• Metadata must be orchestrated through pipelines, not manual documentation
• There must be a clear connection between metadata, downstream analytics, and the Power BI semantic model

All Silver → Gold transformations must be implemented in dbt, with dbt jobs containing the core modeling and business logic to demonstrate a repeatable, production‑ready Medallion pattern.

 

Copilot usage is required and must be intentional.

 

• Copilot should be used multiple times during data preparation
• At least two transformations must be generated or meaningfully enhanced by Copilot
• Transformations must go beyond renaming (e.g., business logic, structural changes, data quality improvements)
• We evaluate how Copilot helped, not just whether it appeared

The end‑to‑end flow must culminate in Power BI configured with Prep data for AI, including:

 

• A simplified schema that guides Copilot
• AI instructions defining business meaning and rules
• At least one verified answer with a human‑approved visual, trigger phrases, and storage in the semantic model

This is where your architecture, metadata, and modeling decisions come together.

 

🎥 What You Must Submit

 

Video Walkthrough (Required)

 

Each team must submit an end‑to‑end video walkthrough showing:

 

• The Medallion architecture and pipelines in Fabric Data Factory
• The metadata pipeline and how metadata flows with the data
• The dbt job transforming Silver → Gold in the Data Warehouse
• Copilot assisted transformations
• The final AI-ready Power BI experience
• Note: you can paste a video URL directly into the description of your entry

Clear storytelling matters just as much as technical correctness. Submit your entries in the AI-ready Data Gallery.

 

🏆 How Submissions Will Be Evaluated

 

Entries will be judged on:

 

• Clarity and correctness of the Medallion architecture
• Quality and intentionality of the metadata pipeline
• Effective use of Copilot in transformations
• Strength of Power BI AI readiness (schema, instructions, verified answers)
• Overall storytelling and reusability of the pattern

🎁 What You Win

 

• Bragging rights
• Exclusive swag
• A chance to work directly with the Data Integration product team
  

More importantly, you’ll help define what “AI-ready analytics” actually looks like in Fabric. We can't wait to see what you all come up with!!

 

Have questions? Drop them here in this thread and we'll answer as soon as we can!