Why Data Quality Checks Belong Upstream of the Warehouse
Most analytics teams discover data problems at the worst possible time, after a dashboard has already shown a stakeholder the wrong number. The postmortem usually follows a familiar arc. Someone notices revenue looks off. An analyst digs through a dbt model. Eventually, the trail leads back to a source table where a field silently changed type, or a batch job dropped a chunk of rows three days...
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