sql-analysis

name	sql-analysis
description	Master SQL for data analysis with complex queries, joins, aggregations, window functions, and query optimization.

SQL Analysis

Master SQL for extracting, transforming, and analyzing data using complex queries, joins, aggregations, and advanced SQL techniques.

When to Use This Skill

• Data extraction
• Business reporting
• Ad-hoc analysis
• Data exploration
• Metric calculation
• Customer segmentation
• Funnel analysis
• Cohort analysis

Core Concepts

1. Complex Joins

-- Customer purchase analysis with multiple joins
SELECT 
    c.customer_id,
    c.name,
    COUNT(DISTINCT o.order_id) as total_orders,
    SUM(oi.quantity * oi.price) as total_revenue,
    AVG(o.order_total) as avg_order_value
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
LEFT JOIN order_items oi ON o.order_id = oi.order_id
WHERE o.order_date >= '2024-01-01'
GROUP BY c.customer_id, c.name
HAVING COUNT(DISTINCT o.order_id) >= 3
ORDER BY total_revenue DESC;

2. Window Functions

-- Monthly revenue with running total and growth
SELECT 
    DATE_TRUNC('month', order_date) as month,
    SUM(order_total) as monthly_revenue,
    SUM(SUM(order_total)) OVER (
        ORDER BY DATE_TRUNC('month', order_date)
    ) as running_total,
    LAG(SUM(order_total)) OVER (
        ORDER BY DATE_TRUNC('month', order_date)
    ) as prev_month_revenue,
    ROUND(
        (SUM(order_total) - LAG(SUM(order_total)) OVER (ORDER BY DATE_TRUNC('month', order_date))) 
        / LAG(SUM(order_total)) OVER (ORDER BY DATE_TRUNC('month', order_date)) * 100,
        2
    ) as growth_pct
FROM orders
GROUP BY DATE_TRUNC('month', order_date)
ORDER BY month;

3. CTEs (Common Table Expressions)

-- Customer cohort retention analysis
WITH first_purchase AS (
    SELECT 
        customer_id,
        MIN(order_date) as cohort_month
    FROM orders
    GROUP BY customer_id
),
monthly_activity AS (
    SELECT 
        fp.customer_id,
        fp.cohort_month,
        DATE_TRUNC('month', o.order_date) as activity_month,
        EXTRACT(MONTH FROM AGE(o.order_date, fp.cohort_month)) as months_since_first
    FROM first_purchase fp
    JOIN orders o ON fp.customer_id = o.customer_id
)
SELECT 
    cohort_month,
    months_since_first,
    COUNT(DISTINCT customer_id) as active_customers
FROM monthly_activity
GROUP BY cohort_month, months_since_first
ORDER BY cohort_month, months_since_first;

Best Practices

1. Use CTEs - Readable, maintainable complex queries
2. Index aware - Understand query performance
3. **Avoid SELECT *** - Specify needed columns
4. Comment queries - Explain business logic
5. Test incrementally - Build queries step by step
6. Handle NULLs - Use COALESCE, proper joins
7. Aggregate before join - Reduce data volume
8. Use EXPLAIN - Analyze query plans

Resources

• Mode SQL Tutorial: https://mode.com/sql-tutorial/
• SQL Style Guide: https://www.sqlstyle.guide/