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Data Quality Scorer

This runs the framework's check logic (ported from lib/dq_checks.py): freshness and completeness classification, a z-score row-count anomaly test, and a 0–100 quality score. Move the sliders to score a table.

1. Table signals

Staleness (hours) 6

Null rate 2%

Documentation 80%

Row count vs baseline 1.0x

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Freshness–

Completeness–

Row-count anomaly–

Documentation–

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Included with Data Quality Monitoring Framework

The complete Data Quality Monitoring Framework package includes:

Databricks Asset Bundle
Metadata-driven scans
A dependency-free checks library

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