SQL Analytical functions are a set of powerful tools that enable data analysts and developers to perform complex calculations and transformations on groups of data, without the need for complex sub-queries or multiple queries. They allow you to perform calculations over a set of rows that are related to each other.
Analytical functions can be used to perform a wide range of tasks, including ranking, partitioning, and aggregation of data. They can be used in conjunction with GROUP BY, ORDER BY, and HAVING clauses to create complex queries that provide valuable insights into your data.
Some of the most commonly used analytical functions in SQL include:
RANK() – This function assigns a rank to each row in a result set based on the value of a particular column or set of columns.
DENSE_RANK() – This function is similar to RANK(), but it assigns consecutive ranks to rows with the same value.
ROW_NUMBER() – This function assigns a unique row number to each row in a result set.
LAG() – This function returns the value of a column from the previous row in a result set.
LEAD() – This function returns the value of a column from the next row in a result set.
FIRST_VALUE() – This function returns the first value of a column in a result set.
LAST_VALUE() – This function returns the last value of a column in a result set.
Analytical functions can be a powerful tool for working with complex data sets, but they require a solid understanding of SQL syntax and functions. They can also be resource-intensive, so it’s important to use them judiciously and optimize your queries for performance.