Data Quality Monitoring
Automated data quality checks run hourly via cron to ensure the integrity of the data warehouse. Checks include freshness validation (is data up-to-date?), completeness (are all coins present?), schema validation (no unexpected NULLs?), anomaly detection (unusual price spikes?), and referential integrity across fact and dimension tables.
Quality Summary
Aggregate quality score per table. The score represents the percentage of checks that passed. Green (≥80%), yellow (≥60%), red (<60%).
Quality Checks
Individual check results with timestamps and details. Each row represents a single quality assertion — passed, warning, or failed.