Build Your Data Quality Deck

Reflecting on past experiences with Magic: The Gathering, the author draws parallels between the strategic deck-building of the card game and the meticulous process of ensuring data quality in Airflow pipelines. Emphasizing the importance of data quality for reliable analytics and AI, the article discusses how Airflow's SQL check operators can be utilized to catch specific data anomalies, such as volume spikes, schema drift, and business rule violations, within a data pipeline. The text introduces six SQL operators—each likened to a strategic card in a deck—that perform various data quality checks, from verifying exact values to monitoring temporal changes. The author stresses the necessity of both Dag-level and platform-level checks to prevent the propagation of faulty data, advocating for a layered approach to data validation. The metaphor extends further with a browser-based game, "Data Quality Duel," designed to help users learn about these operators interactively. The narrative closes by underscoring the critical nature of sequencing checks and the decision-making involved in choosing the appropriate level of intervention when a check fails.