Scoring Methodology — Overview¶
This section documents how the WAF Assessment Tool scores your Databricks workspace across the four Well-Architected Framework pillars. Each pillar has its own scoring page describing the SQL logic, control thresholds, and remediation guidance.
How scoring works¶
The WAF Reload job (waf_reload.py) runs a set of SQL queries against Databricks system tables and stores the results in waf_cache — a catalog created in your workspace during installation.
For each control:
- A scoring query is executed against system tables (billing, compute, information_schema, access).
- The result is expressed as a percentage (0–100).
- The percentage is compared against a threshold defined per control.
- If the percentage meets or exceeds the threshold, the control is marked Met; otherwise Not Met.
Data freshness
Scores reflect the state of your workspace at the time the WAF Reload job last ran. Re-run the job from the Databricks App or the WAF Reload job directly to refresh.
Pillars¶
| Pillar | WAF ID Prefix | Controls | Cache Table |
|---|---|---|---|
| Data & AI Governance | DG- |
8 | waf_controls_g |
| Cost Optimization | CO- |
8 | waf_controls_c |
| Performance Efficiency | PE- |
7 | waf_controls_p |
| Reliability | R- |
7 | waf_controls_r |
Score interpretation¶
| Score vs Threshold | Status |
|---|---|
score >= threshold |
Met |
score < threshold |
Not Met |
Governance column naming
The waf_controls_g table uses a description column where other pillar tables use best_practice. This is because the governance controls were built from a different source schema. The scoring logic and Met/Not Met semantics are identical across all pillars.