Demo 07
End-to-End Data Engineering
Run a real miniature data pipeline: ingest CRM and orders data, catch quality problems, quarantine bad rows, clean and transform, build a gold fact/dimension model, and read analytics-ready output, with schema-drift detection, incremental loads, lineage and monitoring.
Demo Safe sample data only. Deterministic client-side logic. No live model calls.
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.
Orchestration
Data quality
Monitoring
| 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 |
Business model (Gold)
A simplified dimensional model: one fact table joined to conformed dimensions.
Lineage
Click a table or metric to trace where it came from.
Analytics-ready output
Revenue by region
Revenue by category
From demo to production
| 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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