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Data Engineering

The layer everything else depends on. Incremental, tested and traceable — so when a number looks wrong, you can prove where it came from instead of arguing about it.

Step through the pipeline ↓ Read the case study

LIVE DEMO · STEP THROUGH

Watch a production run, layer by layer.

A production run, layer by layer: incremental ingest with schema drift, a quality gate that quarantines a failing row, SCD type 2 on the dimension, and a governed serving view with a freshness SLA. Click a layer, or run the whole pipeline.

sample data

WHAT'S INCLUDED

A warehouse your
whole business can trust.

Every downstream dashboard, forecast and AI agent is only as good as this layer, so it's built to be tested, documented and boring in the best way.

SQL ServerT-SQLETL DevelopmentStar Schema Microsoft FabricAzure Data FactoryDataflows Gen2Git
  • Medallion architectureBronze stays append-only as the lineage anchor; silver is cleaned and conformed; gold is modelled for the business. Every number in a report traces back to the source row it came from.
  • Incremental loadsWatermark-based ingestion so runs stay minutes, not hours, as volume grows — with schema drift logged rather than silently failing the pipeline.
  • Quality gates, not silent dropsTests run before data reaches the modelled layer. Failing rows are quarantined with the test that caught them, because discarding records is how totals stop reconciling.
  • Slowly changing dimensionsHistory preserved with SCD type 2, so a customer changing segment doesn't quietly rewrite last year's reports.
  • Governed serving layerRevenue defined once, in one view, that every report reads — plus freshness SLAs, lineage and failure alerting.

Data scattered across too many systems?

Let's talk about what a single, trusted source of truth would look like for your business.

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