Comparing Window Functions vs Aggregate Functions in SQL for Data Analysis

When working with data in SQL, understanding how to summarize or analyze data is essential. Two powerful tools for this are Aggregate Functions and Window Functions. Although they might seem similar, they serve different purposes and can be used to gain different insights.

Aggregate Functions like SUM(), AVG(), COUNT(), MAX(), and MIN() combine multiple rows into a single summary value. These functions reduce the number of rows by grouping data. For example, if you want the total sales per product, aggregate functions are the way to go.

Window Functions, on the other hand, perform calculations across a set of rows related to the current row but do not reduce the number of rows returned. This makes window functions very useful when you want to keep the original data but add extra information, like ranking, running totals, or averages over a partition.

Let's see both in action using a simple sales data example.

CREATE TABLE sales (
  id INT,
  product VARCHAR(50),
  sales_amount DECIMAL(10,2)
);

INSERT INTO sales VALUES
(1, 'Product A', 100),
(2, 'Product A', 150),
(3, 'Product B', 200),
(4, 'Product B', 300),
(5, 'Product C', 250);

### Example 1: Using Aggregate Functions to get total sales per product

SELECT product, SUM(sales_amount) AS total_sales
FROM sales
GROUP BY product;

This query groups the data by each product and sums the sales amounts, resulting in one row per product.

### Example 2: Using Window Functions to get total sales per product for each row

SELECT id, product, sales_amount,
       SUM(sales_amount) OVER (PARTITION BY product) AS total_sales_per_product
FROM sales;

Here, the window function computes the total sales per product, but each row of the original table is kept. This is useful when you want detailed data alongside aggregated information.

### Key differences summarized:

- Aggregate functions collapse multiple rows into a single output row per group. - Window functions add aggregated information without reducing the number of rows. - Use aggregate functions for summary reports. - Use window functions when you need to keep row-level data but add analytics like rankings, running totals, or averages.

Mastering both aggregate and window functions will elevate your SQL data analysis, enabling you to extract meaningful insights efficiently.