Last updated: May 28, 2026

Scanner Documentation

The AI/BI Readiness Scanner is a Databricks Solution Accelerator package for read-only diagnostic analysis of AI/BI readiness, metric consistency, query intent, and shadow dependencies.

Package Contents

• Setup and permission check notebook.
• Query pattern clustering notebook.
• Metric conflict detection notebook.
• Dashboard dependency notebook.
• Shadow dependency notebook.
• Genie readiness scoring notebook.
• Report generator notebook.
• Synthetic BI metadata example.
• Permission guide and troubleshooting guide.

Operating Modes

Mode	Inputs	Output
Basic	Query history and information schema where available.	Repeated query patterns and candidate metric conflicts.
Full	Query history, billing, lineage, audit, compute, Lakeflow/jobs tables, and optional BI metadata.	Readiness report, dashboard dependency map, shadow dependency report, and transformation backlog.
Lite fallback	Manually exported query or BI metadata sample.	Limited candidate findings with lower confidence labels.

Setup Overview

1. Create or select a customer-owned Unity Catalog output schema.
2. Grant least-privilege read access to the relevant Databricks system tables.
3. Import the scanner notebooks into the customer workspace.
4. Run notebooks in order from 00_setup_and_permissions.py through 06_report_generator.py.
5. Review findings with business, platform, governance, or audit owners.

Minimum Basic Permissions

• SQL warehouse CAN USE.
• SELECT on system.query.history.
• USE CATALOG and USE SCHEMA on the output location.
• CREATE TABLE and MODIFY in the output schema.

Important Limitations

• System table data is not real time, so recent events may not appear immediately.
• Lineage is treated as evidence with confidence labels, not perfect truth.
• Metric conflict detection produces candidates that require human validation.
• The scanner-lite package does not provide a final audit opinion or legal advice.