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Data Quality

Data quality is the practice of ensuring that data is accurate, complete, consistent, timely, and trustworthy as it flows through pipelines and systems. Without explicit quality gates, bad data propagates silently - corrupting dashboards, training flawed models, and breaking downstream consumers. This skill covers the five pillars: schema validation at ingress, expectation-based testing with Great Expectations, data contracts between producers and consumers, lineage tracking for impact analysis, and continuous monitoring for anomaly detection.

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