Window functions are functions that operate on a subset of rows, called a window, that are related to the current row. Unlike aggregate functions, window functions do not reduce the number of rows in the result set, but rather return a value for each row based on the window. Window functions can be used to perform tasks such as ranking, partitioning, calculating running totals, or finding the first or last value in a group.

Aggregate functions are functions that take a set of values and return a single value that summarizes them. For example, you can use aggregate functions to calculate the sum, average, count, minimum, or maximum of a column. Aggregate functions are often used with the GROUP BY clause, which divides the data into groups based on one or more columns, and applies the aggregate function to each group.

One of the main benefits of window functions is that they allow you to perform complex calculations that would otherwise require multiple queries or subqueries. For example, you can use a window function to calculate the percentage of total sales for each product category in one query, instead of having to join the sales table with a subquery that calculates the total sales. Window functions also preserve the original rows and columns in the result set, which can make it easier to analyze and compare the data.

One of the main drawbacks of window functions is that they can be more difficult to write and understand than aggregate functions. Window functions require you to specify the window definition, which can include clauses such as PARTITION BY, ORDER BY, and RANGE or ROWS. These clauses determine how the window is defined and ordered for each row, and can affect the performance and accuracy of the function. Window functions also have some limitations, such as not being able to reference other window functions or columns that are not in the SELECT or ORDER BY clauses.

One of the main benefits of aggregate functions is that they are simple and intuitive to use. Aggregate functions can be applied to any column or expression, and do not require any additional clauses or parameters. Aggregate functions can also be combined with other SQL clauses and operators, such as HAVING, WHERE, or CASE, to filter or condition the data. Aggregate functions are also widely supported by most SQL dialects and platforms.

One of the main drawbacks of aggregate functions is that they can reduce the granularity and detail of the data. Aggregate functions return a single value for each group or for the entire data set, which means that you lose the individual rows and columns that make up the group. Aggregate functions can also be inefficient or inaccurate when used with complex or nested queries, especially if you need to join multiple tables or subqueries to get the desired result.