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/