Data Quality Watchtower
A monitoring assistant that detects schema drift, anomalies, and suspicious dataset changes before pipelines break.
Prototype
Data Tool
ML
A monitoring assistant that detects schema drift, anomalies, and suspicious dataset changes before pipelines break.
Problem. Data issues usually surface downstream after dashboards, models, or reports are already wrong. Why now. Data quality remains operationally important even for AI-native products.
30-day verdict history
0 runs
History builds as the nightly scan runs.
No scans recorded yet.
System status
unknown
Mode
live
Tier A workload
Last check
never
nightly drift scan
Uptime · 30d
—
scan cadence
Schema
v1
public contract
Built for
Data teams, analytics engineers, ML ops teams
Engine
Python
DuckDB
Great Expectations
Pandas
What the engine does
The capabilities running in the nightly drift scan.
- Profile tabular datasets and schemas
- Detect drift and row-level anomalies
- Generate plain-language incident summaries
- Store historical validation results
- Compare saved profiles for schema, null-rate, outlier, and cardinality drift
- Gate releases on dataset drift thresholds