Why Learn SQL Window Functions in 2025?

SQL window functions provide some extremely powerful and convenient features for data practitioners. This article explains why you should learn window functions and when you should use them. I’ll also provide some examples to help you visualize the concepts.

Window functions are part of advanced modern SQL – knowledge of which will definitely enrich the repertoire of any SQL user. Window functions have a broad range of uses and can be applied to almost all facets of business. You’ll often use them when carrying out in-depth business intelligence and data analysis.

The Big Data market is projected to see 100% revenue growth by 2027 . According to the 2024 Gartner Report, data analytics will play an even more critical role in business decision-making. The report states that 72% of companies plan to increase their investment in data analytics tools, and 65% of businesses that have adopted advanced analytics report significant improvement in decision-making speed and accuracy. That is why SQL is so important.

SQL is one of the world’s top programming languages. The need for efficient and scalable database functionalities has never been greater, but some SQL practitioners are still reluctant to learn window functions; they’re seen as something of an intrusion to SQL. In this article, we’ll discuss the benefits of windows functions and show how you can use them to solve real-world business problems.

SQL Window Functions: A Primer

Window functions compute a function or calculation on a set of related rows, allowing you to perform sophisticated statistical analyses using simple, streamlined queries. Some of the most common use cases for window functions are generating rankings, performing time series analysis, finding the difference between rows, and computing running totals, moving averages, etc. All these functionalities are now absolutely essential for business data analytics and generating complex reports or metrics in SQL. We will briefly explore a few of these use cases later in this article. 

SQL window functions help focus on a particular portion of the result set (i.e. the “window”), rather than on the entire result set. In other words, SQL window functions turn a result set into several smaller sets and return values based on these sets.

A Brief History of Window Functions

Window functions are part of modern advanced SQL. The language’s functionality and capabilities have increased in leaps and bounds since its introduction in the 1970s – and later the major revision of the SQL standard in 1992 (SQL-92), which we still use today.

Window functions were added to the SQL standard in 2003; they’ve been part of SQL for nearly two decades now.

Oracle added window functions capabilities in 1998. SQL Server began first supporting them in 2005 with ranking functions, followed by full support in 2012. PostgreSQL added support in 2009, and MySQL added support for window functions in 2018 (through MySQL 8.0). In 2021, all major database management systems now support window function syntax. If you are interested in a more detailed walkthrough, see our earlier article on The History of SQL Standards.

“A lot has changed since SQL-92”, says Markus Winand, the founder of Modern SQL and an advocate for the need to learn SQL beyond its basics and adopt its newer functionalities. He also authored the book SQL Performance Explained, which focuses on SQL indexing (and its application in performance tuning), window functions, scalability, testing, and clustering data. If you are still only using the SQL-92 language reference, then you are overlooking many of the great features introduced in later evolutions such as window functions – and, to stay competitive in 2021, you must take on this challenge and embrace it!

The SQL window functions course offered by LearnSQL.com provides comprehensive coverage of this topic. Its sizable collection of interactive exercises will help you build practical understanding of this skill. But why should you invest your time in learning window functions?

Benefits of Using Window Functions

The amount of data we generate has been constantly increasing – according to Forbes, it’s over 2.5 quintillion bytes of data a day! When it comes to managing and analyzing data at scale, window functions can provide some unique advantages, including:

• Combining aggregate and non-aggregate values without collapsing:
  Window functions are useful when you do not want to collapse rows in the result set. Instead of a single output row, a single value for each of the rows from the underlying query is You can choose to keep all the columns from each row and add additional values calculated by the window function. This is a major advantage when you need both aggregate and non-aggregate values in one table. Window functions are similar to aggregate functions but with the added benefit that they have access to data within the individual rows. They do not cause rows to become grouped into a single output row, which makes them quite powerful. The first row is treated differently in window functions, especially in relation to functions like LAG() and LEAD(), where it is assigned a NULL value when shifting data.

• Simple syntax:
  The simple syntax of window functions makes it much easier to maintain and update large queries in the production pipeline. Most of the things you can do with window functions are also possible without them, but window functions make your queries simpler and more readable than, say, queries that use subqueries and CTEs to do the same thing. The LAG() function retrieves the value from the previous row, which is particularly useful in time-series analyses and comparisons.
• Faster performance:
  SQL queries with window functions typically tend to have better computational performance and scalability than similar queries without them (e.g. alternative methods using self-joins, subqueries, or cursors).

All these performance advantages will surely help make your life much easier, whether you’re using data analysis at work or on personal projects.

Use Cases for Window Functions

Now, let’s explore a few examples of how window functions are actually implemented in practical business contexts:

• Creating rankings:
  SQL ranking functions are great for ordering, sequencing, and comparing data across various factors/features. For example, the window function RANK() can be used to find the top 10 best-selling products or the 30 employees with the highest number of sales. Additionally, ranking functions can also recognize ties – e. if two products are tied as your best sellers, both of those will get the rank number 1. You can choose whether the row after the tied values gets the next sequential rank (i.e. assigning it a rank value of 2 using the DENSE_RANK() function) or whether the next rank is skipped (i.e. assigning it a rank value of 3 using the regular RANK() function).
• Running totals and moving averages:
  Running totals and moving averages are some of the most commonly used KPIs in business, sales, and marketing performance measurements across all organizations. Computing such cumulative figures is much simpler with window functions. For example, you may want a running total of the number of new subscribers at the end of each month and a moving average of active subscribers over 4 weeks through the Check out our earlier article on SQL running totals and SQL moving averages for some walkthroughs.
• Time-series analysis:
  Imagine you’re analyzing the propagation of COVID-19 across countries and regions over With SQL window functions, you could easily calculate the percentage change in daily confirmed cases using LAG(). You could analyze general trends in case counts and fatalities based on countries and their sub-regions on a daily, weekly, or monthly level. One of our earlier articles was on analyzing COVID-19 time series with SQL window functions. Hopefully, by the middle of 2021 we could be using the same functionality to chart the decrease in cases worldwide, as vaccination programs are implemented at a global scale.

Time-series can also be used to visualize product demand trends across time or analyze time-variant aspects such as seasonality. Such analyses are widely used for products and services heavily influenced by seasonal demand, helping businesses customize their offerings to maximize their bottom line over the course of a year.

To get a deeper understanding of the practical use cases of SQL window functions, check out the article When Do I Use SQL Window Functions?

Where to Learn More About Window Functions

By now, you probably realize the importance of having SQL window functions as a part of your data science and data analysis toolkit. While this was a relatively quick overview of the history, benefits, and use cases of SQL window functions, I hope it has piqued your interest.

Honestly, there is no better way to master SQL window functions than by understanding them and then practicing them – and for that, LearnSQL.com’s SQL window functions course is the place to go.

The course offers a structured approach, guiding you from the basics to more advanced applications of window functions. You'll find interactive exercises that allow you to practice key concepts in real-world scenarios, which is the best way to build a solid understanding.

Not only will you learn how to create rankings, calculate running totals, and perform time-series analysis, but you'll also get hands-on experience that will make you confident in using window functions in any data project.

Enroll today and take the next step in advancing your SQL skills! Plus, there's a free trial available, so you can start learning risk-free and see the benefits for yourself.