Boost Your SQL Expertise: Unleash the Power of Window Functions for Advanced Data Analysis!

A vital feature that enables sophisticated data analysis and manipulation within SQL queries is the SQL window functions. Eight frequently used SQL window functions will be covered in detail in this extensive guide, along with practical examples to help you comprehend how to utilize them and realize their full potential. We’ll make a table with 20 rows of sample data to make it easier for you to practice the examples we’ve covered.

Creating a Sample Dataset:

Let’s start by establishing a table named “Students” to serve as a model dataset. Twenty rows of information will be presented in this table, each with the following columns: StudentID, StudentName, Category, Sales, OrderDate, OrderTime, and OrderAmount. Here is an illustration of a SQL query to make the table and fill it with information:

CREATE TABLE Students (
? StudentID INT,
? StudentName VARCHAR(50),
? Category VARCHAR(50),
? Sales INT,
? OrderDate DATE,
? OrderTime TIME,
? OrderAmount DECIMAL(10,2)
);


INSERT INTO Students (StudentID, StudentName, Category, Sales, OrderDate, OrderTime, OrderAmount)
VALUES
? (1, 'John Doe', 'Electronics', 1000, '2023-05-01', '08:00:00', 100.00),
? (2, 'Jane Smith', 'Electronics', 800, '2023-05-01', '09:00:00', 200.00),
? (3, 'Alice Johnson', 'Apparel', 1200, '2023-05-01', '10:00:00', 150.00),
? (4, 'Bob Williams', 'Apparel', 900, '2023-05-01', '11:00:00', 300.00),
? (5, 'Sarah Davis', 'Electronics', 650, '2023-05-02', '08:00:00', 125.00),
? (6, 'Michael Brown', 'Electronics', 450, '2023-05-02', '09:00:00', 175.00),
? (7, 'Emily Wilson', 'Apparel', 700, '2023-05-02', '10:00:00', 100.00),
? (8, 'Daniel Taylor', 'Apparel', 550, '2023-05-02', '11:00:00', 225.00),
? (9, 'Olivia Martinez', 'Electronics', 550, '2023-05-03', '08:00:00', 180.00),
? (10, 'James Anderson', 'Electronics', 350, '2023-05-03', '09:00:00', 210.00),
? (11, 'Sophia Thomas', 'Apparel', 900, '2023-05-03', '10:00:00', 120.00),
? (12, 'David Garcia', 'Apparel', 400, '2023-05-03', '11:00:00', 275.00),
? (13, 'Emma Hernandez', 'Electronics', 250, '2023-05-04', '08:00:00', 130.00),
? (14, 'Alexander Martinez', 'Electronics', 800, '2023-05-04', '09:00:00', 190.00),
? (15, 'Mia Johnson', 'Apparel', 600, '2023-05-04', '10:00:00', 160.00),
? (16, 'William Davis', 'Apparel', 350, '2023-05-04', '11:00:00', 220.00),
? (17, 'Ava Wilson', 'Electronics', 700, '2023-05-05', '08:00:00', 140.00),
? (18, 'Joseph Anderson', 'Electronics', 550, '2023-05-05', '09:00:00', 200.00),
? (19, 'Samantha Thomas', 'Apparel', 500, '2023-05-05', '10:00:00', 170.00),
? (20, 'Benjamin Smith', 'Apparel', 400, '2023-05-05', '11:00:00', 240.00);
        

This query creates the “Students” table and inserts 20 rows of sample data, similar to the examples discussed in the guide. Now, let’s explore the eight commonly used SQL window functions and their corresponding queries:

1. ROW_NUMBER(): The ROW_NUMBER()?function assigns a unique sequential number to each row within a partition. Here’s an example query that uses ROW_NUMBER() to rank students by their sales:

SELECT ROW_NUMBER() OVER (ORDER BY Sales DESC) AS Rank
? ? ? ?StudentName,
? ? ? ?Sales
FROM Students        

This query would result in the following table:

2. The RANK() and DENSE_RANK()?functions give each row within a partition a rank based on a predetermined order. Here is an example query that ranks students within each category based on their sales using the functions RANK() and DENSE_RANK():

SELECT RANK() OVER (PARTITION BY Category ORDER BY Sales DESC) AS Rank
? ? DENSE_RANK() OVER (PARTITION BY Category ORDER BY Sales DESC) AS DenseRank,
? ? StudentName,
? ? Category,
? ? Sales
FROM Students        

This query would result in the following table:

3. LAG() and LEAD(): The LAG() and LEAD()?functions allow you to access the value of a column from a previous or next row within a partition. Here’s an example query that uses LAG() and LEAD() to calculate the difference in sales compared to the previous and next rows:

SELECT StudentName,Sales
? ? ? ?LAG(Sales) OVER (ORDER BY Sales) AS PreviousSales,
? ? ? ?LEAD(Sales) OVER (ORDER BY Sales) AS NextSales
FROM Students,        

This query would result in the following table:

4. FIRST_VALUE() and LAST_VALUE(): The FIRST_VALUE() and LAST_VALUE() functions are powerful tools that allow us to retrieve the first and last values within a specified window. In our case, we want to determine the first and last sales amounts for each day in our dataset.

To achieve this, we will use the FIRST_VALUE() function to retrieve the first sales amount of each day and the LAST_VALUE() function to retrieve the last sales amount of each day. Let’s take a look at the modified query:

SELECT StudentName,Sales,OrderDate,
? ? ? ?FIRST_VALUE(Sales) OVER (PARTITION BY OrderDate ORDER BY OrderTime) AS FirstSalesOfDay,
? ? ? ?LAST_VALUE(Sales) OVER (PARTITION BY OrderDate ORDER BY OrderTime ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS LastSalesOfDay
FROM Students        

This query would result in the following table:

In this query, we introduce the concept of window framing using the ROWS BETWEEN clause. By specifying?ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING, we ensure that the window for the LAST_VALUE() function includes all rows within the same OrderDate partition.

Now, let’s briefly discuss the term “UNBOUNDED.” In the context of window framing, UNBOUNDED represents the absence of a boundary or limit. When we specify UNBOUNDED PRECEDING, it means that the window includes all rows from the start of the partition, and when we specify UNBOUNDED FOLLOWING, it means that the window includes all rows until the end of the partition.

By utilizing the UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING keywords, we ensure that the LAST_VALUE() function considers all rows within the same OrderDate partition when determining the last sales amount of each day.

The resulting table will display the OrderDate, FirstSalesOfDay (representing the amount of the first sale for each day), and LastSalesOfDay (representing the amount of the last sale for each day). This information can provide valuable insights into daily sales trends and patterns.

5. The NTILE() method?assigns a group number to each row after dividing the rows into a predetermined number of groups. Here is an example query that uses NTILE() to categorize students according to their sales into three quartiles:

SELECT StudentName,Sales,
       NTILE(3) OVER (ORDER BY Sales) AS Quartile
FROM Students        

This query would result in the following table:

6. PERCENT_RANK():?The PERCENT_RANK() function determines the percentage-based relative rank of a row within a partition. Using PERCENT_RANK(), the following example query determines the percentile rank of each student’s sales:

SELECT StudentName,Sales,
       PERCENT_RANK() OVER (ORDER BY Sales) AS PercentRank
FROM Students        

This query would result in the following table:

To ascertain the relative rank of a specific value within a set, we can utilize the PERCENT_RANK() method. It determines the proportion of rows above or below a certain row. This can be helpful in a variety of circumstances, such as determining the percentile rank of a student’s sales relative to others.

7. CUME_DIST():?The cumulative distribution of a value within a partition using the CUME_DIST() function. Using CUME_DIST(), the following example query determines the cumulative distribution of revenues for each student:

SELECT StudentName,Sales,
? ? ? ?CUME_DIST() OVER (ORDER BY Sales) AS CumulativeDistribution
FROM Students        

This query would result in the following table:

When we wish to determine the cumulative distribution of a specific value inside a dataset, the CUME_DIST() method comes in handy. It reveals where a value stands in relation to others in terms of cumulative percentage.

The CUME_DIST() function can be helpful in various situations, such as determining how a student’s sales rank in relation to the distribution as a whole. The proportion of values less than or equal to a specific value is represented by the cumulative distribution value, which is assigned and ranges from 0 to 1.

By using CUME_DIST(), We may acquire a thorough knowledge of how a particular number relates to the entire dataset in terms of its cumulative position by using CUME_DIST(). This knowledge is beneficial for evaluating the relative importance or performance of a data point in relation to the overall dataset.

8. PARTITION BY clause with aggregate functions:?In Microsoft SQL Server, the WINDOW clause is not supported. However, we can still achieve similar functionality by using the PARTITION BY clause directly within each window function. This allows us to perform calculations over specific partitions of data.

To see how the PARTITION BY clause may be utilized successfully, let’s look at an example:

SELECT StudentName,Category,
? ? ? ?SUM(Sales) OVER (PARTITION BY StudentName, Category) AS TotalSales
FROM Students        

This query would result in the following table:

In this query, the PARTITION BY clause is used within the SUM() function. By specifying the columns?StudentName?and?Category?in the PARTITION BY clause, we create partitions or groups of data based on unique combinations of these columns. The SUM() function is then applied to each partition, calculating the total sales for each combination of?StudentName?and?Category.

By utilizing the PARTITION BY clause, we can achieve similar functionality to the WINDOW clause in other database systems. It allows us to perform aggregations, calculations, or ranking operations within specific partitions of our data.

Although the syntax differs slightly from the WINDOW clause, the PARTITION BY clause in Microsoft SQL Server allows us to achieve the desired results efficiently.

In summary

In this thorough publication, we’ve covered eight frequently used SQL window functions and given valuable examples to assist you in learning how to utilize them. You may do sophisticated data analysis, sorting, aggregation, and more in your SQL queries by learning these window functions. You can put each of the examples described to practice using the given table, which has 20 rows of sample data, which will help you understand and become more adept at using SQL window functions. With this information, you may improve your SQL abilities and handle challenging data problems with ease.