DataCheck — Data Quality & Validation

DataCheck

Organizations building data infrastructure face quality challenges: **Silent Data Degradation**: Data quality issues don't trigger alerts like application errors. A vendor API starts returning null values for previously populated fields. An upstream service changes timestamp format from ISO8601 to Unix epoch. Record counts drop 30% without explanation. Your pipeline continues running successfully—processing garbage in, producing garbage out. Downstream teams discover problems days or weeks later when reports look wrong or models perform poorly. **Manual Validation Doesn't Scale**: Data engineers write custom SQL queries checking for expected conditions, but maintaining hundreds of validation rules across dozens of tables is unsustainable. Checks run on schedules (daily, hourly) missing issues occurring between runs. When checks fail, debugging requires manual investigation—was this a data issue, pipeline bug, or infrastructure problem? You're spending 30-50% of engineering time on data quality firefighting. **ML Model Drift Goes Undetected**: Models trained on historical data degrade when production data distribution changes. If training data had users aged 18-65 but production data now includes minors, models predict incorrectly. Feature distributions shift (average transaction value doubles) invalidating model assumptions. Without comparing production data against training baselines, you discover model degradation only through business metrics (conversion drops, customer complaints)—after damage is done.