Adventures in Machine Learning

Mastering SQL Window Functions: Advanced Data Manipulation Techniques

Introduction to SQL Window Functions

Structured Query Language (SQL) is used to manage and manipulate data in almost every IT environment nowadays. SQL window functions are a relatively new addition to SQL that help users to perform various operations on a set of data within a specified window of values.

In other words, window functions enable users to perform advanced calculations on the chosen data, without altering or grouping the existing data set. This article will provide a thorough explanation of SQL window functions, the differences between window functions and aggregate functions, and some practical use cases of window functions.

Further, we will explore how RANK functions can be used as a basic example to rank salaries within departments, and the subsequent challenge of ranking employees by position.

Explanation of SQL Window Functions

SQL window functions allow users to perform operations based on a set of data within a specific window, relative to the current row. The current row is the focus of the intermediary dataset, and by utilising window functions, users can access data from tracks that are specified in the particular windowing frame.

Window functions do not change the underlying data but rather provide a means to generate additional outputs, ranking, data division, and summaries. SQL window functions comprise a partitioning clause, which is then followed by an order by everything, which specifies the ordering and framing of the data.

The aim is to extract a selected range of rows from which the function must derive its output. As a result, window functions differentiate the dataset and produce new results, providing quick and useful insights.

Difference between Window Functions and Aggregate Functions

Aggregate functions, such as SUM, AVG, and COUNT offer summary metrics, combining the data within the entire query result to achieve output. Meanwhile, window functions are analytic, which means they operate on a group of rows, returning a single value for each row.

Therefore, the difference between window functions and aggregate functions lies in the scope of operation.

Use Cases of SQL Window Functions

SQL window functions are used to generate summary metrics or perform advanced analysis within a subquery. Specifically, SQL window functions tackle running totals, ranking, and comparing results to row subsets.

They are also utilised for reconciling and building conditional thresholds, and identifying overlaps in the datasets. Below are discussed, some potential and practical uses of window functions.

Running Totals

Running totals or aggregations of a dataset are an essential use case for SQL window functions. Among the most common and requested running totals calculations include among others, sums, averages, frequencies, and percentage aggregations.


Ranking is a fundamental use of window functions that operates on a window or a subset of rows in the dataset.

Ranking calculations can aid in identifying levels of performance, such as rank of the best performers and the lowest performers.

The top or bottom performers can be compared to the median or average performers, providing useful insights into the dataset.

Conditional Filtering

Window functions can perform sub-querying and conditional filtering on the original dataset. For instance, it can generate a deviation/ratio column that subtracts a previous row to provide insights into a trend in trends in a time-series data set.


Overlapping is an essential window function that compares the current row to a subset of rows within the window of values. This comparison can aid in identifying data relationships, generating recommendations, and insights into trends and patterns.

SQL Window Function Example with RANK

Now that we have a good understanding of SQL window functions, an example of the RANK window function can be explored.

Ranking functions can be utilised to rank or order a dataset based on a selected column/condition.

For example, we can choose to use the RANK function to rank employee salaries within departments. Let’s consider an employee table with the following fields:

EmployeeID, EmployeeName, Salary, Department.

We can then use the following SQL statement to rank employee salaries within departments. “`

SELECT EmployeeName,





FROM Employee;


This will execute a sub-query on Employee table, creating a window for each department ordered by the employee’s salary in descending order.

The RANK function assigns a rank to each employee based on their salary relative to all other employees within their parent department, generating a new rank column.

Challenge to Rank Employees by Position

However, if multiple employees in a department hold the same salary, the window function will generate duplicate rank numbers assigned to each of the respective employees. This can pose a ranking challenge because traditionally only one employee holds a given rank at any one time.

To overcome the challenge and assign unique ranks to employees based on their position, we could utilise the DENSE_RANK function. For example, the revised SQL statement would look like this:


SELECT EmployeeName,





FROM Employee;


By replacing RANK with DENSE_RANK, the function assigns a unique rank to each employee based on their salary, resulting in no duplicate ranks.


In summary, SQL window functions are powerful tools that help users to perform advanced calculations on a specified window of data within a dataset. They can be used to create groups, perform running totals, ranking, conditional filtering, and overlapping.

Window functions are different from aggregate functions in that they only operate on a selected subset of data within a set. An example of the RANK function revealed challenges to ranking employee data in cases of employee records with the same salary, which can be resolved with DENSE_RANK.

By utilising window functions in SQL, users can perform much advanced calculations and manipulation of datasets.

SQL Window Function Example with Math Expression

While RANK and DENSE_RANK are popular window functions used in SQL, a math expression is another excellent tool that can be utilised to perform calculations on selected columns within a specific window. In the following example, we can use a math expression to find where employee salaries rank in relation to their department’s top salary.

Suppose we have an employee table that consists of the following fields: EmployeeID, EmployeeName, Salary, and Department. To determine where each employee’s salary ranks in comparison to their department’s highest salary, we can use the following SQL statement:


SELECT EmployeeName,



(Salary/MAX(Salary) OVER (PARTITION BY Department))*100 as Percentile_Rank

FROM Employee;


This SQL statement calculates a math expression using an employee’s salary and the maximum salary of their respective department.

The MAX function partitions and finds the highest salary by department, allowing us to perform calculations only within our specified subquery or frame. The math expression then takes the employee’s salary and divides it by the department’s top salary, multiplied by 100 to get a percentile rank.

This calculation provides insights into where an employee stands in comparison to their department’s highest earners. Additionally, grouping by the department attribute provides users with an idea of which department has higher-paid employees and provides useful insights for further analysis.

SQL Window Function Example with Train Schedule

In this section, we will discuss the use of SQL window functions in a train schedule database, specifically exploring the LEAD function to calculate “time to next station” and the MIN function to calculate “elapsed travel time”.to Train Schedule Database

The train schedule database consists of a schedule table with the following fields: StationName, TrainLine, ArrivalTime. The arrival time is a datetime field that describes when a train is expected to arrive at a particular station.

Use of LEAD Window Function to Calculate “Time to Next Station”

The LEAD window function allows us to access data from the row following the current row within the window. In the train schedule database, we can use the LEAD window function to determine “time to next station”, which is the time difference between the current and subsequent arrival times.


SELECT StationName,



LEAD(ArrivalTime) OVER (PARTITION BY TrainLine ORDER BY ArrivalTime) – ArrivalTime AS TimeToNextStation

FROM Schedule;


This SQL statement uses the LEAD function to calculate “TimeToNextStation” by comparing each station’s current time to the next station’s time within the same train line. The partitions identify the train line, allowing us to compute “TimeToNextStation” separately for each train line.

The “ORDER BY” statement sorts the arrival times within each train line, thus producing an ordered frame of data. Use of MIN Window Function to Calculate “Elapsed Travel Time”

In contrast, the MIN window function allows users to access data from the row preceding the current row within their window.

We can use the MIN window function to determine the “elapsed travel time” for each train line.


SELECT StationName,



MIN(ArrivalTime) OVER (PARTITION BY TrainLine ORDER BY ArrivalTime) – ArrivalTime AS ElapsedTravelTime

FROM Schedule;


This SQL statement calculates “ElapsedTravelTime” by comparing each station’s arrival time to the earliest station’s time in the same train line.

The “PARTITION BY” keyword separates each respective train line’s data, and “ORDER BY” sorts their arrival times within each respective train line frame of data. Subtracting the arrival time at each given station from the earliest recorded arrival time within the respective train line offers a useful way to calculate the elapsed travel time.


In conclusion, SQL window functions are instrumental in providing additional insights into sets of data. Window functions such as RANK and DENSE_RANK are commonly used to partition and rank data within a frame, while math expressions are useful for performing calculations based on selected columns and their relationship to other records in the data set.

Furthermore, we’ve proven how window functions like LEAD and MIN can be valuable in specific domains such as train scheduling, which gives rise to the need for functions that provide insights into both forward and backward-related data points. By leveraging SQL window functions, users can extract valuable insights that assist in successful and satisfying data management.

Additional Resources for Learning SQL Window Functions

SQL window functions are powerful tools that offer flexibility in data manipulation and analysis. For users just starting with SQL window functions or looking to expand their existing knowledge, there is a wealth of resources available to help them learn and become proficient in using window functions.

Importance and Flexibility of SQL Window Functions

One of the main benefits of SQL window functions is their ability to perform advanced calculations and provide additional insights that would be otherwise challenging to obtain. By using window functions, users can compute running totals, rank data, calculate percentages, conditional aggregations, and perform time-related analytics.

SQL window functions are also highly flexible, allowing users to work with a vast amount of data and target specific sections within those data sets. By using window frames, users can partition data into discrete sections based on defined parameters, such as date range, numerical order, and column value.

The frames can be tailored to the use case, giving users fine-grained control over computations and analysis. Other Clauses and Topics Related

to SQL Window Functions

In addition to the partition clause that is used in conjunction with SQL window functions, there are several additional clauses and topics that users should be familiar with when working with SQL window functions.

The frame clause controls which rows to include in the window used to perform the calculation. While the partition clause separates the dataset into partitions for each computation, the frame clause specifies the range of rows within each partition included in the calculation.

The order clause specifies the order in which rows for each window should be processed. The rows are sorted based on the specified column(s) and can be ordered in ascending or descending order.

Other related topics include aggregate and scalar functions, joins, subqueries, and NULL handling. Understanding these key concepts related to SQL window functions is essential in becoming proficient in using window functions.

Recommendation to Take Window Functions Course offers a comprehensive online course on SQL window functions, providing users with a structured and interactive learning experience to master window functions in SQL. The course is designed to cater to all users, from beginners to advanced SQL users looking to refine their window function capabilities.

The course content includes an introduction to window functions, an explanation of the SQL partition clause, how to use ranking functions, the various analytic functions available, and examples of practical applications. The interactive nature of the course offers hands-on demonstrations, giving users the opportunity to practice using SQL window functions in a structured environment.

The course also provides quizzes and exercises to test users’ knowledge and skills, ensuring that they have a solid understanding of the concepts covered.


SQL window functions are essential tools in data analysis and manipulation, offering a flexible and powerful way of drilling down into data sets. Understanding the various clauses, topics, and functions related to SQL window functions is crucial in becoming proficient in using window functions. offers a valuable resource to learn window functions in SQL with their comprehensive online course, providing users with a structured learning path to becoming an expert in SQL window functions. In conclusion, SQL window functions are incredibly powerful tools for advanced data manipulation and analysis.

These functions offer flexibility and insight that is difficult to achieve with traditional query methods. By using clauses such as partition and frame, users can easily calculate running totals, ranking data, conditional filtering, and more.

The importance of understanding these key concepts is clear, including the use of math expressions, LEAD, MIN, and other window functions. For those looking to learn SQL window functions, we recommend taking the Window Functions course.

With the right knowledge and skills, SQL window functions can make data analysis more efficient and insightful, leading to a better understanding of the insights and trends within datasets.

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