One malformed record breaks a pipeline. Thousands corrupt a model. Catch them at the source.
Based on Gartner's AI-Ready Data framework
Data must conform to expected structures. A string where you expect an integer breaks everything downstream.
Expanso enforces schemas declaratively. Non-conforming records route to dead-letter queues - they never reach your warehouse.
Data must match expected patterns. A negative price or future birthdate indicates a problem.
Expanso validates against reference data, lookup tables, and business rules. Anomalies caught before they propagate.
You need to know when data quality degrades. Silent failures are the worst failures.
Real-time visibility into data quality across your distributed footprint. Alerts fire when quality drops.
Schema mismatches cause 3am pages. Root cause analysis takes hours - bad data buried in millions of records.
Invalid data caught at source. Pipelines don't break. Bad records route to dead-letter queues with full context.
Problems discovered days later during analysis. Bad data already in reports, dashboards, ML models.
Quality checked continuously. Issues caught immediately. Bad data never reaches downstream.
Data quality is a black box. Teams don't know there's a problem until something breaks.
Real-time dashboards across all streams. Alerts when quality degrades. Problems visible before damage.
Schemas, validation rules, quality thresholds - all in YAML. No custom code.
Rules apply across all sources. Consistent qualification, managed centrally.
Invalid data routes to dead-letter queues with full context. Fix at your convenience.
POS, web, mobile records validated at origination. Duplicates, nulls, invalid formats caught before CDP.
Sensor data validated against expected ranges. Faulty sensors identified before corrupting models.
Transactions validated for format, completeness, business rules. Invalid records flagged.