SQL Programming Resources on Telegram: “SELECT MIN(price) AS lowest

SELECT
    MIN(price) AS lowest_price
FROM products;

1️⃣1️⃣ MAX()

MAX() finds the largest value.

SELECT
    MAX(salary) AS highest_salary
FROM employees;

SELECT
    MAX(amount) AS largest_order
FROM orders;

1️⃣2️⃣ Using Multiple Aggregate Functions

You can use several aggregate functions in the same query.

SELECT
    COUNT(*) AS total_orders,
    SUM(amount) AS total_revenue,
    AVG(amount) AS average_order_value,
    MIN(amount) AS smallest_order,
    MAX(amount) AS largest_order
FROM orders;

This single query gives you a basic sales summary.

1️⃣3️⃣ Aggregate Functions With WHERE

Example:

Analyze completed orders only.

SELECT
    COUNT(*) AS completed_orders,
    SUM(amount) AS revenue,
    AVG(amount) AS average_order_value,
    MIN(amount) AS smallest_order,
    MAX(amount) AS largest_order
FROM orders
WHERE order_status = 'Completed';

This is a powerful analytical pattern.

1️⃣4️⃣ NULL and SUM()

SUM() generally ignores NULL values.

Suppose:

amount 1000, 2000, NULL, 3000

Then: SUM(amount) = 6000

However, if all values are NULL, the result can be NULL rather than 0.

You can handle that later using COALESCE().

Example:

SELECT
    COALESCE(SUM(amount), 0) AS total_revenue
FROM orders
WHERE order_status = 'Completed';

1️⃣5️⃣ Aggregate Functions Are the Foundation of KPIs

Most business dashboards are built using aggregate functions.

For example:

• Revenue = SUM(amount)

• Number of Orders = COUNT(*)

• Customers = COUNT(DISTINCT customer_id)

• Average Order Value = AVG(amount)

• Largest Order = MAX(amount)

This is why mastering aggregates is critical.

1️⃣6️⃣ Calculating Average Order Value

A common e-commerce KPI is AOV — Average Order Value.

A simple version:

SELECT
    AVG(amount) AS average_order_value
FROM orders
WHERE order_status = 'Completed';

Another formulation is:

SELECT
    SUM(amount) / COUNT(*) AS average_order_value
FROM orders
WHERE order_status = 'Completed';

The AVG() version is usually clearer when each row represents one order.

1️⃣7️⃣ Calculating Revenue Per Customer

Suppose the business asks:

What is the average revenue generated per unique customer?

You need to be careful not to divide revenue by the number of orders.

SELECT
    SUM(amount) /
    COUNT(DISTINCT customer_id) AS revenue_per_customer
FROM orders
WHERE order_status = 'Completed';

This is a good example of translating a business metric into SQL.

1️⃣8️⃣ Aggregate Functions + Expressions

You can aggregate calculations.

Example:

SELECT
    SUM(quantity * unit_price) AS total_sales
FROM order_items;

SQL first evaluates: quantity * unit_price for each row, then sums those values.

1️⃣9️⃣ Aggregate Functions + CASE

You can create conditional metrics.

Example:

SELECT
    COUNT(*) AS total_orders,
    SUM(
        CASE
            WHEN order_status = 'Completed'
            THEN 1
            ELSE 0
        END
    ) AS completed_orders
FROM orders;

This technique becomes extremely important when building dashboards.

2️⃣0️⃣ Example: Success Rate

Suppose you have payment transactions. You want:

Percentage of successful transactions.