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Experience 1 · Live Data Pipeline
DATA PIPELINE PROCESSING - FROM RAW DATA TO TRUSTED NUMBERS

Raw data arrives from all your data sources and makes its way through the Medallion Architecture, providing a centralised, unified, governed and auditable foundation for reporting and AI. Every check runs in the open and bad rows are quarantined with reasons. A real pipeline, running live, on the dataset below.

Download the simulated dataset (CSV, 1,000 rows) , the exact data the simulation runs, mess included
THE MEDALLION ARCHITECTURE
BRONZE
Raw, exactly as received
SILVER
Validated & cleaned
GOLD
Business-ready
REPORTING & AI
Live tiles
QUALITY CHECKS: EVERY ROW, EVERY RULE
Waiting for data…
QUARANTINE
Rows that fail a check land here, with their reason.
Nothing is silently dropped. Every row is kept, explained, and auditable.
THE BUSINESS VALUE
  1. Every dataset, from every source, is processed the same unified, governed way.No more per-system workarounds or tribal knowledge. One repeatable process means new data sources are onboarded in days rather than months, and every team can trust what comes out the other end.
  2. Nothing is silently dropped: bad rows are quarantined with reasons, so issues are visible and fixable.Problems get fixed at source instead of surfacing in front of a customer or an auditor, protecting revenue, reputation and compliance in one move.
  3. Data quality is measured, not assumed, with a score you can track and improve.Quality becomes a managed KPI. You can evidence improvement to the board and to regulators, instead of hoping the numbers are right.
  4. Bronze preserves the original data untouched, so you can always reprocess and prove lineage.Complete lineage means faster, cheaper audits, and if rules or requirements change, history can be reprocessed without ever going back to the source systems.
  5. The gold model is analytics- and AI-ready: reports and assistants read it without rework.New dashboards and AI use cases launch in days because the modelling work is already done, cutting the time-to-value of every future data project.