Case study
Data warehouse rebuild
The situation
The warehouse's loading pipelines had been built years earlier as a single serial batch process: extract, transform, load, one table after another, on a fixed nightly clock. That design worked when the data volume was small enough to finish comfortably inside the window. It stopped working gracefully as both the number of source tables and the rows per table grew, and there was no mechanism to catch a bad load before it reached the tables reports queried the next morning, only whoever noticed a number looked wrong.
The approach
We redesigned the loading pipelines around incremental, partition-aware processing rather than reprocessing full tables every run: each load only touches the rows that actually changed, which keeps the work proportional to what happened since the last run instead of proportional to the size of the whole table. Independent tables load in parallel rather than in a fixed serial order, so one slow source no longer holds up every table behind it in the queue.
We added automatic data quality checks at the point of ingestion, row-count reconciliation against the source, null and range checks against columns that should never be null or out of range, and referential checks against the dimensions a fact table depends on, along with schema drift detection so a source system silently adding, dropping or retyping a column produces an alert rather than a quietly wrong load. A load that fails a check is quarantined rather than written through: the previous good data stays in place and the team is paged, so the first anyone hears of a data problem is an alert, not a report that does not add up.
What changed
The loading pipeline now scales with what actually changed rather than with the size of the whole warehouse, and a bad load is caught and quarantined at ingestion instead of silently reaching the tables reports run against. The team is alerted when a check fails, rather than finding out from whoever happens to notice a wrong number downstream.