## Window Functions:

Window Functions are analytics functions that allow the computation of values over groups of rows, which are called windows. The window functions are specified using the OVER clause in the SELECT statement.

## The syntax of the OVER clause is as follows:

OVER ([PARTITION BY partition_expression,] [ORDER BY order_expression [ASC|DESC],] [ROWS {UNBOUNDED|n} PRECEDING>] [ROWS {UNBOUNDED|n} FOLLOWING>])

The `partition_expression`

specifies the grouping of rows, `order_expression`

specifies the ordering of rows, and `ROWS`

specifies the range of rows in the current window. Window Functions vs.

## GROUP BY

The GROUP BY statement aggregates rows based on a common key value. The result is a single row for each unique key value.

In contrast, window functions retain all rows in the result set and then apply the function to each row based on a grouping of rows. Window Functions Advantages:

## Cleaner and More Readable Code:

Window functions enable us to simplify queries by eliminating the need for self-joins and subqueries.

They also improve the readability of the query as the computation is performed in a single statement. Complex Calculations with Fewer Lines of Code:

Window functions are particularly useful for time-series data analysis, where we may require rolling computations such as moving averages, running totals, and rankings.

As such calculations usually require multiple lines of code when using subqueries or self-joins, window functions offer significant benefits. For instance, a running total calculation using a self-join would require multiple lines of code; however, the same calculation can be achieved using a window function and only a single line of code.

The window function can also calculate the ranking of items based on a set of criteria, which can be useful for various purposes such as comparison and benchmarking. Examples of Window Functions:

## Ranking Functions:

Ranking functions assign a rank value to each row within a defined partition.

The rank value indicates the positional rank of the row relative to the other rows within the partition. The most commonly used ranking functions are ROW_NUMBER, RANK, and DENSE_RANK.

ROW_NUMBER assigns a unique sequential integer to each row within the result set. The row number starts at 1 for the first row and increments for each subsequent row.

RANK and DENSE_RANK assign a rank value to each row based on the ordering of the values in the ORDER BY clause. Aggregate Functions:

Aggregate functions calculate the result based on a group of rows.

We can use them in combination with window functions to aggregate a subset of rows. The most commonly used aggregate functions are SUM, AVG, COUNT, and MAX/MIN.

## OVER QUERY:

Let’s see an example using an OVER clause to calculate the running total of sales for each quarter.

`SELECT quarter, sales, SUM(sales) OVER (ORDER BY quarter) from sales_data;`

This query calculates the running total of sales for each quarter by using the SUM function in combination with the OVER clause.

The OVER clause defines the order in which the rows are computed for the SUM function.

## Conclusion

In multiple ways and applications, window functions offer significant performance and readability advantages over traditional SQL queries that use subqueries or self-joins. By using partitioning and ordering within a single query statement, window functions enable us to perform complex and sophisticated calculations with fewer lines of code.

The use of window functions becomes even more critical as the size of the data set increases, and queries become more complex, thus offering significant advantages to data professionals. Types of Window Functions:

There are several types of window functions available in SQL.

## The most commonly used types of window functions are:

## Aggregate Functions:

Aggregate functions compute a single result value from a set of input values. They perform calculations across multiple rows, and each row in the result set may contain the same aggregate value.

Examples of aggregate functions include SUM, AVG, MIN, MAX, and COUNT. Ranking Window Functions:

Ranking window functions assign a rank value to each row in the result set based on a specified order, such as numerical or alphabetical order.

The most commonly used ranking functions are ROW_NUMBER, RANK, and DENSE_RANK. Positional Window Functions:

Positional window functions allow you to select a specific row in the result set based on its position.

Examples of positional functions include LAG and LEAD. Distribution Functions:

Distribution functions calculate how data is distributed within a set of values.

Examples include CUME_DIST and PERCENT_RANK. Overview of Famous Window Functions:

## SUM:

The SUM function calculates the sum of a column for a group of rows.

It can be used in combination with the OVER clause to calculate running totals, moving averages, and other time-series calculations. AVG:

The AVG function calculates the average value of a column for a group of rows.

It can be used in combination with the OVER clause to calculate moving averages and other time-series calculations. MIN and MAX:

The MIN and MAX functions calculate the smallest and largest value in a column for a group of rows.

They can be used in combination with the OVER clause to calculate running minimum and maximum values. COUNT:

The COUNT function calculates the number of rows in a group.

It can be used in combination with the OVER clause to count rows for each group. CUME_DIST and PERCENT_RANK:

The CUME_DIST and PERCENT_RANK functions calculate the relative position of a row within a group of rows based on the value of a specified column.

CUME_DIST returns the cumulative distribution of a value within a group, while PERCENT_RANK returns the relative position of the row within the group. ROW_NUMBER, RANK, and DENSE_RANK:

ROW_NUMBER assigns a unique, sequential integer to each row within a result set, while RANK and DENSE_RANK assign a rank value based on the ordering of the values in the ORDER BY clause.

## NTILE:

The NTILE function divides a result set into a specified number of ordered buckets or groups. This function is useful for grouping data based on percentiles or other ordered criteria.

## FIRST_VALUE and LAST_VALUE:

The FIRST_VALUE and LAST_VALUE functions return the first and last value in a specified column for each row in the result set. They are useful for retrieving the first and last record for a specific group or partition.

## LEAD and LAG:

The LEAD and LAG functions allow you to access data from a previous or subsequent row in the result set. LEAD retrieves the value from the next row, and LAG retrieves the value from the previous row.

## Examples:

## Total number and mean of new clients for each salesman:

Suppose we have a table called Sales with columns called Salesman, Quarter, and NewClients. We can use the GROUP BY statement and window functions to calculate the total number and mean of new clients for each salesman:

`SELECT Salesman, Quarter, SUM(NewClients) OVER (PARTITION BY Salesman) AS TotalNewClients, AVG(NewClients) OVER (PARTITION BY Salesman) AS MeanNewClients FROM Sales`

This query groups the rows by Salesman, calculates the total and mean number of NewClients for each salesman using the SUM and AVG aggregate functions in combination with the OVER clause.

## Salesman Ranking per Each Quarter:

## We can use ranking functions to calculate the ranking of a salesman based on their sales performance in each quarter:

`SELECT Salesman, Quarter, ROW_NUMBER() OVER (PARTITION BY Quarter ORDER BY Sales DESC) AS Rank FROM Sales`

This query ranks salesmen based on their sales performance in each Quarter, using the ROW_NUMBER function in combination with the OVER clause. Grouping Salesmen by Their Performance:

## We can use the NTILE function to group salesmen based on their sales performance:

`SELECT Salesman, Sales, NTILE(4) OVER (ORDER BY Sales DESC) AS Quartile FROM Sales`

This query divides salesmen into four groups based on their Sales performance, using the NTILE function in combination with the OVER clause.

## Number of Acquired Clients from Previous Quarter:

We can use the LAG and LEAD functions to retrieve values from the previous or next rows in the result set, respectively. For example, to calculate the number of acquired clients from the previous quarter, we can use the LAG function in combination with the OVER clause:

`SELECT Quarter, NewClients, LAG(NewClients) OVER (ORDER BY Quarter) AS Previous_NewClients FROM Sales`

This query returns the NewClients value for each Quarter, as well as the value from the previous quarter using the LAG function in combination with the OVER clause.

## Conclusion:

Window functions provide powerful features that enable complex calculations in SQL. They can simplify queries, reduce the number of lines of code, and improve the performance of analytics queries.

By using window functions in combination with other SQL constructs such as GROUP BY and ORDER BY, we can perform sophisticated analyses on our data and gain deeper insights into our data’s trends and patterns. Importance of Learning Window Functions:

Learning how to use window functions is an essential tool for anyone working with data and SQL.

By mastering this skill, you can perform complex calculations with ease, efficiently analyze trends and patterns, and create more concise and maintainable code. The demand for window function skills in the job market is rapidly increasing.

Employers are looking for candidates with advanced technical skills, including expertise in analytics and database management. As more and more companies adopt big data and analytics tools, the need for skilled professionals who can interpret and analyze the data continues to rise.

Fortunately, there are numerous resources available to learn window functions. Online tutorials, interactive courses, and certifications are great learning tools to help you advance your skills in this vital area.

Platforms such as LearnSQL.com provide a comprehensive set of interactive courses that teach you how to use window functions in SQL and how to apply them to real-world analytics challenges. Besides, popular database management systems like MS SQL Server, PostgreSQL, and Oracle support window functions, meaning that there are plenty of opportunities to apply this skill in professional settings across many different industries.

Finally, there are some best practices to consider when working with window functions. Some of these include using partitioning and ordering clauses effectively, explicitly defining the columns being selected, and testing your queries using a small subset of data before running them on large sets of data.

With all these benefits, it’s easy to see why learning window functions is a must for anyone working with data and SQL. Embracing this skillset and keeping up with industry trends can help advance your career, improve your data analysis skills, and ultimately lead to more significant insights and value from your data.

In summary, window functions in SQL provide powerful analytical capabilities that can simplify complex calculations, improve query performance, and create more readable code. They come in different types, including ranking, aggregate, positional, and distribution functions, and each has a specific use case.

Learning window functions is an essential skill for anyone working with data and SQL as it can help you advance your career and provide more significant insights and value from your data. By utilizing available resources such as interactive courses and hands-on experience, you can master the skill and become an expert in analytics and database management.

Ultimately, incorporating window functions into your SQL arsenal allows you to analyze trends and patterns more efficiently and create concise and maintainable code, leading to better decision making and improved business outcomes.