aa803bd3bd
Expand to Business Analytics: Add Customer, Sales, and KPI models
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Major expansion from fraud detection to comprehensive business analytics:
DATABASE CHANGES:
- Renamed database from 'fraud_detection' to 'business_analytics'
- Renamed user from 'fraud_analyst' to 'data_analyst'
- Expanded from 20 to 39 tables across 4 business models
NEW MODELS (19 tables):
1. Customer Analytics (5 tables):
- customer_segments, customer_lifetime_value, churn_predictions
- customer_satisfaction, engagement_metrics
2. Sales & Revenue Analytics (6 tables):
- product_catalog, sales_transactions, sales_targets
- sales_performance, revenue_forecasts
3. KPI & Metrics (8 tables):
- kpi_definitions, daily_metrics, monthly_summaries
- trend_analysis, dashboard_snapshots
- report_definitions, report_executions, data_quality_checks
DATA GENERATION:
- Extended generate_data.sh with 6 new steps (now 15 total)
- Added CLV calculations for all customers
- Added churn predictions based on transaction recency
- Added 30K customer satisfaction surveys
- Added 1M sales transactions linked to 24 products
- Added 90 days of daily KPI metrics
- Added 24 months of business summaries
SQL EXERCISES (3 new levels):
- Level 2: Customer Analytics (10 exercises + 3 challenges)
- Level 3: Sales & Revenue Analysis (12 exercises + 3 challenges)
- Level 4: KPI Dashboards & Metrics (12 exercises + 3 challenges)
DOCUMENTATION:
- Updated README.md with business analytics focus
- Updated QUICKSTART.md with new data generation steps
- Updated SETUP_COMPLETE.md with 39-table architecture
- Added DATA_MODELS.md with complete model specifications
- Added WHATS_NEW.md with migration guide
SEED DATA:
- Added 8 customer segments (VIP, High Value, etc.)
- Added 24 products across 5 categories
- Added 16 KPI definitions across 4 categories
- Added 8 standard report definitions
All changes maintain idempotency and backward compatibility with existing fraud detection functionality.
2025-10-23 14:17:12 -04:00
b30733ccad
Add complete financial fraud detection database with Docker, schema, data generation, and SQL exercises
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- Docker setup with PostgreSQL 16 and DB-UI web interface
- Comprehensive 20+ table schema with fraud detection patterns
- Idempotent shell scripts for data generation (no Python dependency)
- Realistic geographic data (100 US cities, 210 world cities)
- 5M+ transactions with embedded fraud patterns (velocity, geographic, structuring, etc.)
- Progressive SQL exercises from beginner to advanced fraud detection
- Complete documentation and quick start guide
- Setup and verification scripts
2025-10-23 13:53:30 -04:00
Alphaeus Mote