Try it yourself · 07
Bring your business data together
Try messy sample data. See it checked, cleaned, and turned into useful figures.
Synthetic sample data, made for this demo. Runs in your browser.
Try it yourself
Choose an option or a sample below, then see what changes. No technical knowledge needed.
This interactive demo reproduces the architecture and behaviour of a production data pipeline using safe, synthetic sample data and a reduced dataset. It runs entirely in your browser.
The steps
Data quality
Monitoring
See the data and how it works
| order_id | customer_key | product_key | date_key | quantity | revenue | cost | margin |
|---|---|---|---|---|---|---|---|
| O0001 | C018 | P06 | 2026-06-25 | 11 | 2,210.45 | 1,210 | 1,000.45 |
| O0002 | C006 | P04 | 2026-08-13 | 1 | 392.96 | 260 | 132.96 |
| O0003 | C006 | P02 | 2026-08-13 | 18 | 5,983.98 | 4,320 | 1,663.98 |
| O0004 | C006 | P03 | 2026-08-14 | 3 | 1,416.75 | 960 | 456.75 |
| O0005 | C005 | P05 | 2026-05-29 | 6 | 1,317.76 | 840 | 477.76 |
| O0006 | C019 | P04 | 2026-06-04 | 19 | 7,480.4 | 4,940 | 2,540.4 |
| O0007 | C010 | P05 | 2026-08-13 | 10 | 2,113.76 | 1,400 | 713.76 |
| O0008 | C022 | P06 | 2026-07-22 | 1 | 160.53 | 110 | 50.53 |
How the information fits together
A simplified dimensional model: one fact table joined to conformed dimensions.
Where the information came from
Click a table or metric to trace where it came from.
Useful business figures
Revenue by region
Revenue by category
How this could work for you
| Demo concept | Production equivalent |
|---|---|
| Source loader | Azure Data Factory / Fabric pipeline / API |
| Bronze / raw layer | ADLS / Lakehouse raw zone |
| Validation | dbt tests / custom validation |
| Silver / cleaned layer | Transformed lakehouse tables (Spark / SQL) |
| Gold / business model | Semantic / business models (dbt, warehouse) |
| Orchestration | ADF / Fabric / Databricks workflows |
| Monitoring | Azure Monitor / pipeline telemetry |
Need a pipeline like this on your own data?
This is how MCC turns scattered operational data into a reliable, analytics-ready data platform. Want to talk it through?
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