Difference Between Aggregate and Analytic Functions

The primary distinction between aggregate functions and analytic functions lies in how they handle rows of data. Aggregate functions, like SUM() and COUNT(), condense multiple rows into a single output value per group, effectively collapsing the dataset. For instance, if you aggregate sales data by month, the output will be a single total for each month. In contrast, analytic functions retain the original rows in the output while allowing for calculations across specified groups. For example, using SUM() OVER (PARTITION BY ...) provides a running total for each row rather than just one total per group. This difference is crucial for analysts who need detailed insights alongside aggregated summaries. By understanding when to use each type of function, users can effectively analyze data according to their specific needs, whether summarizing information or retaining granular detail.