EBOOK
Quick Notes: Data Quality
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Data quality is fundamental to trustworthy analytics and AI. When bad data enters your pipelines, the impact goes beyond broken dashboards. Flawed metrics can lead to incorrect operational and financial decisions with real business consequences. This quick notes guide covers common data quality challenges and outlines two practical approaches for enforcing data quality in Airflow before issues reach downstream systems.
You’ll learn how to:
- Apply DAG-level and platform-level data quality checks and when to use each
- Use SQL check operators in Airflow to validate critical data assumptions
- Track key metrics to detect anomalies or unexpected shifts in your data
