The problem is usually trust, not volume
Companies rarely come to us because their data is too big. They come because two reports disagree, nobody can explain which is right, and as a result the leadership team has quietly stopped trusting the dashboards. That is a data engineering problem, not an analytics one.
The fix is a single defined path from source systems to reporting, with transformations that live in version control rather than in someone's spreadsheet formulas. When a number looks wrong you should be able to trace it back through every step to the row it came from. Until that is true, adding more dashboards just multiplies the disagreement.
Pipelines that fail loudly
A pipeline that silently stops is worse than no pipeline, because people keep making decisions on stale numbers without knowing it. We build in schema validation, freshness checks, row-count expectations, and alerting, so a broken load produces a notification rather than a quietly outdated dashboard.
We also design for reprocessing. Source systems get corrected retroactively, business logic changes, and bugs get discovered late — so pipelines need to be able to rebuild a date range from scratch and land on the same answer every time.
On the warehouse side we favour boring, well-understood tooling and clear modelling over novelty. The goal is a system your own team can operate and extend after we hand it over.
