SQL/docs/QUICKSTART.md

Quick Start Guide

🚀 Get Up and Running in 5 Minutes

Step 0: Install Dependencies (Optional - 5 minutes)

If you don't have Docker, PostgreSQL client (psql), or other required tools:

# Make script executable
chmod +x scripts/install-dependencies.sh

# Run the installer
./scripts/install-dependencies.sh

What this installs:

• PostgreSQL client (psql)
• Docker & Docker Compose
• Utility packages (curl, wget, git)

Supported OS: Ubuntu, Debian, CentOS, RHEL, Fedora, Arch Linux, macOS

Note: All other scripts will automatically check for dependencies and prompt you to install them if missing.

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Step 1: Start Docker Containers (1 minute)

# From the project root directory
docker-compose up -d

What this does:

• Starts PostgreSQL 16 database
• Starts DB-UI web interface
• Creates network and volumes

Verify it's running:

docker-compose ps

You should see both business_analytics_db and business_analytics_ui running.

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Step 2: Initialize Database Schema (1 minute)

# Make script executable (first time only)
chmod +x scripts/setup-database.sh

# Run setup
./scripts/setup-database.sh

What this does:

• Creates all 39 tables across 4 business models
• Sets up indexes and constraints
• Loads reference data (countries, merchant categories, customer segments, products, KPIs, etc.)

Expected output:

✓ PostgreSQL is ready
✓ Creating tables, indexes, and constraints
✓ Loading reference data
✓ Database setup completed successfully!

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Step 3: Generate Test Data (15-30 minutes)

# Make script executable (first time only)
chmod +x data/generate_data.sh

# Run data generation
./data/generate_data.sh

What this does:

• Generates 100,000 customers
• Creates 150,000 accounts
• Generates 5,000,000 transactions
• Creates fraud patterns and alerts
• Generates customer analytics (CLV, churn, satisfaction)
• Creates sales data (1M sales records)
• Generates KPI metrics (90 days of daily metrics)

⏱️ Time estimate:

• Fast machine (SSD, 16GB RAM): ~15 minutes
• Average machine: ~20-25 minutes
• Slower machine: ~30 minutes

You can monitor progress: The script shows progress for each step:

[1/15] Loading geographic reference data...
[2/15] Generating Customers...
[3/15] Generating Accounts...
...
[10/15] Generating Customer Lifetime Value data...
[11/15] Generating Churn Predictions...
[12/15] Generating Customer Satisfaction data...
[13/15] Generating Sales Transactions...
[14/15] Generating Daily Metrics...
[15/15] Generating Monthly Summaries...

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Step 4: Access the Database

Option A: DB-UI Web Interface (Recommended for Beginners)

1. Open your browser to: http://localhost:3000
2. You'll see the database tables in the sidebar
3. Click any table to browse data
4. Use the "Custom SQL" tab to run queries

Features:

• Visual table browser
• SQL query editor with syntax highlighting
• Export results to CSV
• Schema introspection

Option B: Command Line (psql)

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

Quick commands:

-- List all tables
\dt

-- Describe a table
\d customers

-- Run a query
SELECT COUNT(*) FROM transactions;

-- Exit
\q

Option C: Your Favorite SQL Client

Connection Details:

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

Popular clients:

• DBeaver (free, cross-platform)
• pgAdmin (free, PostgreSQL-specific)
• DataGrip (paid, JetBrains)
• TablePlus (paid, macOS/Windows)

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🎓 Your First Queries

1. Check Data Counts

-- How many customers?
SELECT COUNT(*) FROM customers;

-- How many transactions?
SELECT COUNT(*) FROM transactions;

-- How many sales records?
SELECT COUNT(*) FROM sales_transactions;

-- How many KPIs are being tracked?
SELECT COUNT(*) FROM kpi_definitions WHERE is_active = TRUE;

2. 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 10;

3. 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;

4. 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;

5. Fraud Detection: Flagged Transactions

SELECT
    t.transaction_id,
    t.amount,
    t.fraud_score,
    t.flagged_reason,
    c.first_name,
    c.last_name
FROM transactions t
JOIN accounts a ON t.account_id = a.account_id
JOIN customers c ON a.customer_id = c.customer_id
WHERE t.is_flagged = TRUE
ORDER BY t.fraud_score DESC
LIMIT 10;

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📚 Next Steps

Start Learning SQL

1. Begin with basics: exercises/01-basic-queries/README.md
2. Progress through levels: Work through exercises 01-06
3. Practice fraud detection: exercises/06-fraud-detection/README.md

Explore the Data

-- What countries are represented?
SELECT country_name, COUNT(*) as customer_count
FROM customers c
JOIN countries co ON c.country_id = co.country_id
GROUP BY country_name
ORDER BY customer_count DESC;

-- What are the top merchant categories?
SELECT mc.category_name, COUNT(*) as transaction_count
FROM transactions t
JOIN merchants m ON t.merchant_id = m.merchant_id
JOIN merchant_categories mc ON m.category_id = mc.category_id
GROUP BY mc.category_name
ORDER BY transaction_count DESC;

-- How many fraud cases by type?
SELECT ft.fraud_name, COUNT(*) as case_count
FROM fraud_cases fc
JOIN fraud_types ft ON fc.fraud_type_id = ft.fraud_type_id
GROUP BY ft.fraud_name
ORDER BY case_count DESC;

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🔧 Troubleshooting

Database won't start

# Check logs
docker-compose logs postgres

# Restart containers
docker-compose restart

Can't connect to database

# Check if PostgreSQL is ready
docker exec fraud_detection_db pg_isready -U fraud_analyst

# Check port is not in use
netstat -an | grep 5432

Data generation fails

# Check disk space
df -h

# Check memory
free -h

# Try with smaller dataset
# Edit data/generate_data.sh and reduce:
NUM_CUSTOMERS=10000
NUM_TRANSACTIONS=500000

Reset everything

# Stop and remove everything
docker-compose down -v

# Start fresh
docker-compose up -d
./scripts/setup-database.sh
./data/generate_data.sh

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💡 Tips

1. Use DB-UI for exploration - Great for browsing and understanding the schema
2. Use psql for practice - Best for learning SQL commands
3. Start simple - Begin with basic SELECT queries before complex joins
4. Check the exercises - They're designed to build your skills progressively
5. Experiment - The database is yours to explore and learn from!

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🎯 Learning Goals

After completing this tutorial, you'll be able to:

• ✅ Write complex SQL queries
• ✅ Understand database relationships
• ✅ Detect fraud patterns in data
• ✅ Use window functions and CTEs
• ✅ Optimize queries with indexes
• ✅ Investigate financial crimes

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Ready to start? Head to exercises/01-basic-queries/README.md! 🚀