Add synthetic demo data notebooks and documentation

README.md

@@ -50,6 +50,10 @@ For subsequent local terminal updates:
 
 Do not run `databricks bundle init`; this project already has its bundle configuration.
 
+## Demo Data
+
+If you do not have a dataset ready, `notebooks/demo-data/` includes standalone Databricks notebooks that generate synthetic Unity Catalog datasets for banking, healthcare, retail, SaaS churn, talent advisory, and wind turbine maintenance demos. See [notebooks/demo-data/README.md](notebooks/demo-data/README.md) for the available schemas, required widgets, permissions, and metric view notes.
+
 ## Prerequisites
 
 Notebook installer:

notebooks/demo-data/README.md

@@ -0,0 +1,50 @@
+# Demo Data Notebooks
+
+These Databricks notebooks generate fictional Unity Catalog datasets for Genie Workbench demos and evaluation. They are useful when users want to try Create Agent, IQ Scan, Quick Fix, or Auto-Optimize but do not have a ready dataset.
+
+Run them from a Databricks workspace. They are Databricks notebook source files and are not intended for local execution.
+
+## Required Widgets
+
+Each notebook defines the same setup widgets:
+
+| Widget | Required | Default | Description |
+|---|---:|---|---|
+| `catalog` | Yes | empty | Unity Catalog where the demo schema will be created. There is no safe default. |
+| `schema` | No | dataset-specific | Schema/database name for the generated tables and metric views. |
+| `overwrite_existing` | No | `false` | When `false`, writes fail if a table already exists. Set to `true` to recreate existing demo tables. |
+
+The notebooks create the schema if it does not exist, then write Delta tables, add table and column comments, register primary/foreign key constraints, and create metric views.
+
+## Permissions
+
+The user running a notebook needs:
+
+- `USE CATALOG` on the selected catalog
+- `CREATE SCHEMA` on the selected catalog
+- Permission to create Delta tables, constraints, comments, and views in the generated schema
+- A Databricks Runtime or SQL warehouse/runtime combination that supports Unity Catalog metric views
+
+Metric views use Databricks `CREATE VIEW ... WITH METRICS LANGUAGE YAML`. See the Databricks docs for [CREATE VIEW](https://docs.databricks.com/aws/en/sql/language-manual/sql-ref-syntax-ddl-create-view) and the [metric view YAML reference](https://docs.databricks.com/aws/en/business-semantics/metric-views/yaml-reference).
+
+## Available Datasets
+
+| Notebook | Default schema | Tables | Metric views | Story |
+|---|---|---|---|---|
+| `generate_banking_data.py` | `horizon_bank` | `products`, `branches`, `customers`, `accounts`, `transactions`, `service_requests` | `mv_banking_transactions`, `mv_customer_health`, `mv_service_quality` | Bank customers shift toward digital channels while a service outage drives complaint spikes and lower satisfaction. |
+| `generate_hospital_readmission_data.py` | `hospital_readmission` | `patients`, `hospitals`, `encounters`, `care_transitions`, `claims`, `readmissions` | `mv_readmission_quality`, `mv_claims_cost`, `mv_care_transitions` | Discharge follow-up, diagnosis mix, and weekend discharges affect 30-day readmission risk and claims cost. |
+| `generate_retail_apparel_data.py` | `retail_apparel` | `products`, `stores`, `customers`, `inventory_snapshots`, `sales`, `returns` | `mv_retail_sales`, `mv_inventory_health`, `mv_retail_returns` | Holiday demand, online growth, clearance activity, stockouts, and returns shape retail revenue and margin. |
+| `generate_saas_churn_data.py` | `saas_churn` | `accounts`, `subscriptions`, `product_usage`, `support_tickets`, `invoices`, `churn_events` | `mv_subscription_revenue`, `mv_product_usage`, `mv_churn_risk` | Product adoption, support severity, billing behavior, and utilization explain SaaS churn and ARR loss. |
+| `generate_talent_advisory_data.py` | `talent_advisory` | Workforce source tables plus curated marts for planning, hiring, retention, mobility, compensation, and succession | `mv_workforce_planning`, `mv_hiring_funnel`, `mv_retention_engagement`, `mv_internal_mobility`, `mv_comp_performance`, `mv_succession_planning` | Workforce planning connects engagement, mobility, compensation, hiring, and succession coverage across business units. |
+| `generate_wind_turbine_maintenance_data.py` | `wind_turbine_maintenance` | `wind_farms`, `turbines`, `components`, `sensor_readings`, `maintenance_events`, `failure_events` | `mv_turbine_performance`, `mv_maintenance_reliability`, `mv_failure_events` | Sensor anomalies, icing, preventive maintenance, and model differences drive turbine failures and downtime. |
+
+## Recommended Flow
+
+1. Open one notebook in a Databricks workspace.
+2. Set `catalog` to a Unity Catalog where you can create schemas and tables.
+3. Optionally change `schema`.
+4. Leave `overwrite_existing` as `false` for the first run.
+5. Run the notebook from the top.
+6. In Genie Workbench, create or optimize a Genie space that references the generated tables or metric views.
+
+To refresh a demo dataset, rerun the notebook with `overwrite_existing` set to `true`.