// Data Quality at Scale with Great Expectations

Data Quality · Monitoring

Data Quality Great Expectations Monitoring Airflow

Great Expectations turns data quality into executable checks: expect column values, distributions, and relationships. The real win is making expectations part of the pipeline—run them in CI or in your DAG so bad data fails fast.

Custom expectations let you encode domain rules (e.g. “this ID must exist in the dimension table”). Start with the built-in suite, then add a few high-impact custom checks rather than hundreds of low-value ones.

Alert fatigue kills data quality programs. Start with a small set of critical expectations and clear ownership; expand only when the team actually acts on failures. Integrate with Slack or PagerDuty only for the checks that need immediate action.

→ Key takeaway: Fewer, high-signal expectations beat a long list of weak checks. Automate in the pipeline and avoid alert fatigue.