Business Analytics - SQL Learning Database

A comprehensive, production-grade database designed for learning SQL through realistic business analytics, customer insights, sales analysis, and fraud detection scenarios.

🎯 Overview

This project provides a complete PostgreSQL database with 5+ million transactions, 39 tables across 4 business models, and progressive SQL exercises aligned with Data Analyst responsibilities. Perfect for:

• SQL Beginners → Learn fundamentals with real-world data
• Data Analysts → Practice customer analytics, sales analysis, KPI dashboards
• Business Analysts → Understand customer behavior and revenue patterns
• Security Professionals → Detect fraud patterns
• Students → Hands-on business intelligence and analytics

📊 Database Statistics

Core Data

• 100,000 Customers with KYC data
• 150,000 Bank accounts (checking, savings, credit)
• 200,000 Payment cards
• 50,000 Merchants across 35 categories
• 5,000,000 Transactions (7% fraudulent)
• 1,000,000 Sales records linked to products
• 500,000 Login sessions

Analytics Data

• 100,000 Customer Lifetime Value calculations
• 100,000 Churn predictions
• 30,000 Customer satisfaction surveys
• 24 Product catalog entries
• 1,500+ Daily KPI metrics
• 24 Monthly business summaries
• 50,000+ Fraud alerts
• 5,000+ Fraud cases

🏗️ Architecture

Data Model Features

• ✅ 39 Tables across 4 business models
• ✅ Foreign key constraints for data integrity
• ✅ Indexes for query performance
• ✅ Realistic geographic data (100 US cities, 210 world cities)
• ✅ Customer analytics (CLV, churn, satisfaction, engagement)
• ✅ Sales analytics (products, targets, forecasts)
• ✅ KPI tracking (daily metrics, trends, dashboards)
• ✅ Fraud detection (velocity, geographic, structuring patterns)
• ✅ Audit trails and compliance tables

Business Models

1. Fraud Detection Model (20 tables)

customers → accounts → transactions → alerts → fraud_cases
              ↓
            cards → merchants

2. Customer Analytics Model (5 tables)

customers → customer_lifetime_value → customer_segments
         → churn_predictions
         → customer_satisfaction
         → engagement_metrics

3. Sales & Revenue Model (6 tables)

product_catalog → sales_transactions → sales_performance
                                    → sales_targets
                                    → revenue_forecasts

4. KPI & Metrics Model (8 tables)

kpi_definitions → daily_metrics → trend_analysis
               → monthly_summaries
               → dashboard_snapshots
               → report_definitions → report_executions
               → data_quality_checks

🚀 Quick Start

Prerequisites

• Docker and Docker Compose
• 8GB RAM minimum
• 20GB disk space

1. Clone the Repository

git clone https://github.com/yourusername/SQL.git
cd SQL

2. Start the Database

docker-compose up -d

This starts:

• PostgreSQL 16 on port 5432
• DB-UI web interface on port 3000

3. Initialize the Schema

chmod +x scripts/setup-database.sh
./scripts/setup-database.sh

4. Generate Test Data

chmod +x data/generate_data.sh
./data/generate_data.sh

⏱️ Note: Data generation takes 15-30 minutes depending on your system.

5. Access the Database

Option A: DB-UI Web Interface

http://localhost:3000

Option B: Command Line

docker exec -it business_analytics_db psql -U data_analyst -d business_analytics

Option C: Your Favorite SQL Client

Host: localhost
Port: 5432
Database: business_analytics
Username: data_analyst
Password: SecurePass123!

📚 Learning Path

Level 1: Basic Queries

• SELECT, WHERE, ORDER BY
• Filtering and sorting
• Basic comparisons
• Location: exercises/01-basic-queries/

Level 2: Customer Analytics ⭐ NEW

• Customer segmentation
• Lifetime value (CLV) analysis
• Churn prediction
• Satisfaction metrics
• Engagement tracking
• Location: exercises/02-customer-analytics/

Level 3: Sales & Revenue Analysis ⭐ NEW

• Sales performance vs targets
• Product analytics
• Channel and regional analysis
• Revenue forecasting
• Profitability analysis
• Location: exercises/03-sales-analysis/

Level 4: KPI Dashboards & Metrics ⭐ NEW

• KPI tracking and monitoring
• Trend analysis
• Dashboard creation
• Data quality monitoring
• Executive reporting
• Location: exercises/04-kpi-dashboards/

Level 5: Advanced SQL Techniques

• Window functions (ROW_NUMBER, RANK)
• Common Table Expressions (CTEs)
• Running totals and moving averages
• Complex aggregations
• Location: exercises/05-advanced-sql/

Level 6: Fraud Detection

• Velocity fraud detection
• Geographic anomalies
• Money mule networks
• Account takeover patterns
• Location: exercises/06-fraud-detection/

💼 Data Analyst Use Cases

This database supports typical Data Analyst responsibilities:

Routine Analysis

• Daily sales summaries
• Customer acquisition metrics
• Transaction volume tracking
• Basic KPI monitoring

Semi-Routine Reporting

• Weekly customer analytics
• Monthly revenue reports
• Product performance analysis
• Churn risk identification

Dashboard Creation

• Executive KPI dashboards
• Sales performance dashboards
• Customer health dashboards
• Operational metrics dashboards

Trend Identification

• Revenue trends (MoM, YoY)
• Customer behavior patterns
• Product sales seasonality
• Engagement score trends

Data Quality

• Missing data detection
• Anomaly identification
• Validation checks
• Data completeness monitoring

🔍 Fraud Patterns Included

1. Velocity Fraud

Multiple rapid transactions from the same account

2. Geographic Impossibility

Card used in different countries within hours

3. Money Mule Networks

Rapid transfer chains between accounts

4. Account Takeover

Sudden changes in transaction patterns

5. Structuring (Smurfing)

Multiple transactions just under $10,000 reporting threshold

6. Card Testing

Multiple small failed transactions

7. High-Risk Merchants

Unusual activity at gambling/crypto merchants

8. Dormant Account Reactivation

Long-inactive accounts suddenly active

📁 Project Structure

SQL/
├── docker-compose.yml          # Docker orchestration
├── docker/
│   └── init/                   # Database initialization
├── schema/
│   ├── 01-create-tables.sql    # DDL (idempotent)
│   └── 02-seed-data.sql        # Reference data
├── data/
│   ├── generate_data.sh        # Data generation script
│   └── reference/              # Geographic data
│       ├── us_cities.csv
│       ├── world_cities.csv
│       └── load_geographic_data.sql
├── scripts/
│   ├── setup-database.sh       # Setup automation
│   └── verify-setup.sh         # Verification script
├── exercises/
│   ├── 01-basic-queries/       # SQL fundamentals
│   ├── 02-customer-analytics/  # ⭐ Customer insights
│   ├── 03-sales-analysis/      # ⭐ Sales & revenue
│   ├── 04-kpi-dashboards/      # ⭐ KPI tracking
│   ├── 05-advanced-sql/        # Advanced techniques
│   └── 06-fraud-detection/     # Fraud patterns
└── docs/
    ├── DATA_MODELS.md          # ⭐ Complete model documentation
    ├── WHATS_NEW.md            # ⭐ Recent changes
    └── QUICKSTART.md           # Quick start guide

🔧 Configuration

Environment Variables

Edit docker-compose.yml to customize:

POSTGRES_DB: business_analytics
POSTGRES_USER: data_analyst
POSTGRES_PASSWORD: SecurePass123!

Data Volume

Modify data/generate_data.sh:

NUM_CUSTOMERS=100000      # Adjust as needed
NUM_TRANSACTIONS=5000000  # Adjust as needed
FRAUD_PERCENTAGE=7        # 7% fraudulent

🔄 Idempotency

All scripts are idempotent - safe to run multiple times:

• setup-database.sh - Drops and recreates schema
• generate_data.sh - Clears and regenerates data
• Schema files use DROP IF EXISTS

🎓 Sample Queries

Customer Analytics: High-Value Customers

SELECT
    c.customer_id, c.first_name, c.last_name,
    clv.clv_score, cs.segment_name
FROM customers c
JOIN customer_lifetime_value clv ON c.customer_id = clv.customer_id
JOIN customer_segments cs ON clv.segment_id = cs.segment_id
WHERE cs.segment_name IN ('VIP', 'High Value')
ORDER BY clv.clv_score DESC
LIMIT 20;

Sales Analytics: Top Products

SELECT
    p.product_name, p.product_category,
    COUNT(st.transaction_id) as sales_count,
    SUM(st.total_amount) as total_revenue
FROM product_catalog p
JOIN sales_transactions st ON p.product_id = st.product_id
GROUP BY p.product_id, p.product_name, p.product_category
ORDER BY total_revenue DESC
LIMIT 10;

KPI Dashboard: Current Status

SELECT
    kd.kpi_name, kd.kpi_category,
    dm.metric_value, kd.target_value,
    dm.status
FROM kpi_definitions kd
JOIN daily_metrics dm ON kd.kpi_id = dm.kpi_id
WHERE dm.metric_date = CURRENT_DATE
  AND kd.is_active = TRUE
ORDER BY kd.kpi_category, kd.kpi_name;

Fraud Detection: Velocity Fraud

SELECT account_id, COUNT(*) as txn_count, SUM(amount) as total
FROM transactions
WHERE transaction_date >= NOW() - INTERVAL '1 hour'
GROUP BY account_id
HAVING COUNT(*) > 5;

Geographic Anomalies

SELECT t1.card_id, c1.country_name, c2.country_name,
       t2.transaction_date - t1.transaction_date as time_diff
FROM transactions t1
JOIN transactions t2 ON t1.card_id = t2.card_id
JOIN countries c1 ON t1.country_id = c1.country_id
JOIN countries c2 ON t2.country_id = c2.country_id
WHERE t1.country_id != t2.country_id
  AND t2.transaction_date BETWEEN t1.transaction_date
  AND t1.transaction_date + INTERVAL '2 hours';

🛠️ Maintenance

Reset Everything

docker-compose down -v
docker-compose up -d
./scripts/setup-database.sh
./data/generate_data.sh

Backup Database

docker exec fraud_detection_db pg_dump -U fraud_analyst fraud_detection > backup.sql

Restore Database

cat backup.sql | docker exec -i fraud_detection_db psql -U fraud_analyst -d fraud_detection

📖 Documentation

• Data Model - Complete ER diagram and table descriptions
• Fraud Patterns - Detailed fraud scenario explanations
• Setup Guide - Detailed installation instructions

🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add exercises or improve data generation
4. Submit a pull request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• PostgreSQL community
• DB-UI project (https://github.com/n7olkachev/db-ui)
• Financial crime investigation best practices

📧 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: /docs folder

---

Happy Learning! 🎉

Start with exercises/01-basic-queries/ and work your way up to detecting sophisticated fraud patterns!