SQL Window Function - Part 2 | How to write SQL Query using Frame Clause, CUME_DIST | SQL Tutorial

Link of Window function part -1
• Mastering SQL Window F...
Understanding and Using SQL Window Functions with Frame Clause and CUME_DIST
Window functions in SQL, also known as analytic functions, are powerful tools that allow you to perform calculations across rows of a result set, creating a virtual "window" of data. They are particularly useful for tasks like:
Ranking: Determining the rank of a value within a group.
Distribution: Calculating the cumulative distribution or percentile rank of a value.
Leading or lagging: Accessing values from preceding or following rows.
Frame Clause: Defining the Window
The frame clause is a crucial component of window functions. It specifies the range of rows that should be included in the calculation. Common frame clauses include:
ROWS: Defines the frame based on the number of rows.
RANGE: Defines the frame based on the values in a specific column.
CUME_DIST: Cumulative Distribution Function
The CUME_DIST() function calculates the cumulative distribution of a value within a partition. It returns a value between 0 and 1, representing the proportion of values that are less than or equal to the current value.
Additional Window Functions
FIRST_VALUE(), LAST_VALUE(), NTH_VALUE(): Return the first, last, or nth value within a window.
NTILE(): Divides the result set into a specified number of groups.
PERCENT_RANK(): Calculates the percentile rank of a value within a partition.
RANK(), DENSE_RANK(): Assign a rank to each row within a partition.
LEAD(), LAG(): Access values from preceding or following rows.
Remember: When using window functions, it's important to understand the impact of the PARTITION BY and ORDER BY clauses on the result set. These clauses define the groups and the order within which the window functions are applied.
By mastering window functions and the frame clause, you can gain valuable insights from your data and perform complex calculations with ease.