Data Quality & Pipeline Observability

How to trust the numbers your pipelines produce: why a green job can still ship wrong data, the checks that catch silent breakage, and the observability that finds it before a human makes a decision on it.

1. Why Trust Is the Whole Product The mental model: data can be broken even when the job is green. A silent data bug - wrong numbers from a pipeline that 'succeeded' - is worse than a loud crash, because nobody knows to look until a decision is made on it.
2. Data Quality Checks The dimensions worth testing automatically - freshness, volume, schema, and validity (nulls, uniqueness, ranges) - where to run them so the pipeline fails fast, and an annotated check that turns a silent data bug into a loud one.
3. Pipeline Observability Seeing the whole system: lineage to trace which downstream tables a broken source poisons, monitoring and alerting on your quality checks, and data SLAs - so the silent failure trips a wire before a human ever reads the bad number. Plus how to avoid alert fatigue.