Window Functions Starter Pack for 2026: Learn One Advanced Skill per Week in January

Window functions are one of the most practical SQL skills you can learn if you already know the basics and want to analyze data more effectively. They help you compare rows, build rankings, and track changes over time without complex subqueries. In this starter pack, you’ll learn one window function skill per week in January and turn an advanced topic into a usable, everyday SQL tool.

Window functions are one of those SQL topics that often get labeled as “advanced” and then quietly postponed. Yet in real analytical work, they are one of the fastest ways to level up. They let you analyze data row by row without losing detail, avoid complex subqueries, and write SQL that actually reflects how you think about data.

January is a good moment to tackle them. New reporting cycles, new projects, and often new learning goals. Instead of trying to learn everything at once, this starter pack approach focuses on one practical skill per week. After one month, you won’t just recognize window functions – you’ll know how to use them.

This article shows how to structure that month and how to use LearnSQL.com resources to make the process efficient and realistic. Each LearnSQL.com resource has a clear role:

• The Window Functions course teaches window function concepts and syntax in detail. Over 200 practical exercises ensure that you reinforce each concept and get plenty of hands-on practice.
• The Window Functions Practice Set builds confidence through repetition, with 100 exercises organized around three realistic, real-world scenarios.
• The Window Functions Cheat Sheet is a downloadable reference that summarizes syntax, key clauses, and the most commonly used window functions. It is available in A4, Letter, A3, Ledger, and mobile-friendly PDF formats for quick lookups while working with window functions.

Together, they form a complete learning loop: learn, practice, apply.

Why Window Functions Are Worth Learning in 2026

Most real-world SQL work is not about totals. It’s about comparisons:

• Who sold more than last month?
• Which product ranks highest in each category?
• How does today’s value compare to the previous row?

Classic GROUP BY queries struggle with these questions because they remove detail of individual rows. Window functions solve this by calculating values across related rows while keeping every original row intact.

Imagine a GROUP BY query with daily revenue per product.

This result is useful, but notice what’s missing:

• Individual days are gone
• You can’t see how revenue changed over time
• You can’t compare one row to another

Now look at the result of a similar calculation using a window function.

Here, every original row is preserved, and an extra analytical column is added. You can immediately see:

• How totals build up over time
• How each row contributes to the final result
• Patterns and trends that GROUP BY hides

This is the core advantage of window functions. They let you calculate rankings, running totals, and moving averages without losing row-level detail.

If you already know basic SQL – SELECT, WHERE, GROUP BY, and JOIN – window functions are the most natural next step for analytics, reporting, finance, marketing performance analysis, and product data work.

Week 1: Learn the Core Syntax and Mental Model

The goal of week one is understanding the concept behind window functions. Window functions look intimidating mainly because they introduce new syntax. Once you understand the idea of a “window,” everything else becomes easier.

A typical window function has this structure:

function_name(column) OVER (...)

The key element is the OVER() clause. It defines the window, meaning the set of rows that the function looks at when performing a calculation.

If you use SUM(revenue) OVER(), the window is the entire result set. The function looks at all rows at once.

Every row stays in the result, and the same total appears next to each one. This allows you to calculate ratios, such as how much each row contributes to the total revenue.

If you use SUM(revenue) OVER (PARTITION BY product), the window is no longer the whole table. Instead, SQL creates a separate window for each product.

Here, rows are grouped logically, but not collapsed. Each row “sees” only the rows from the same product when the calculation is performed.

This is the core mental model of window functions. You define a window of related rows, apply a calculation, and keep all original rows in the result.

Week 2: Ranking and Comparing Rows

Once the basics are clear, it’s time to work to get to know new window functions. Ranking functions are usually the first family of window functions people learn.

This week focuses on ROW_NUMBER(), RANK(), and DENSE_RANK(). These functions are commonly used for leaderboards, top-N queries, and identifying duplicates or first records within a group.

Imagine a table named sales_summary with total revenue per product:

Now consider the following query:

SELECT
  product,
  revenue,
  RANK() OVER (ORDER BY revenue DESC) AS revenue_rank
FROM sales_summary;

In this query, the ranking is based on the revenue column. The OVER (ORDER BY revenue DESC) clause defines the window and the order used only for the ranking calculation, not for sorting the final result set.

Products A and B receive the same rank because they have the same revenue. The next rank is 3 because RANK() leaves gaps when ties occur. Note that the ordering inside OVER() is used only to determine how the ranking is calculated, not how results are displayed.

Here, products A and B share the same rank because they have the same revenue. The next rank is skipped, which is exactly how RANK() behaves. There are other ranking functions that have different behavior: if you used DENSE_RANK() instead, product C would receive rank 2. If you used ROW_NUMBER(), every row would get a unique number, even when values are tied. The key idea stays the same: the OVER (ORDER BY ...) clause defines the window and the order in which rows are compared.

You continue working through the LearnSQL.com Window Functions course, specifically Parts 4 and 5, where ranking functions are explained in detail with practical examples. This is also the right moment to start using the SQL Window Functions Cheat Sheet. The cheat sheet helps you quickly recall syntax and function behavior, so you can stay focused on understanding when to use each ranking function rather than memorizing details.

Week 3: Running Totals and Time-Based Analysis

In week three, window functions start feeling like real analytical tools. This is where you learn to calculate running totals, cumulative sums, and moving averages—patterns that appear constantly in real data work.

Imagine a table with daily revenue:

To calculate a running total, you can use the following window function:

SELECT 
  date, 
  revenue, 
  SUM(revenue) OVER (ORDER BY date) AS running_total 
FROM daily_sales;

Here, the window is defined by ORDER BY date. For each row, the function looks at all rows up to the current date and calculates the cumulative sum. Each row keeps its original data, and the running total shows how values accumulate over time. This would be difficult and much less readable to achieve with subqueries.

This week is about understanding how aggregate functions such as SUM() and AVG() behave when used as window functions, and how ordering rows by date or sequence affects the result.

These patterns are widely used in financial reporting, marketing performance analysis, and product metrics, wherever changes over time matter.

You continue with the LearnSQL.com Window Functions course, focusing on Parts 6 and 7, where time-based analysis and cumulative calculations are explained in detail. This is also the point where practice matters most. The Window Functions Practice Set gives you structured exercises using real datasets, forcing you to think in windows rather than subqueries. Repetition here is what turns understanding into real, usable skill.

Week 4: Combining Window Functions in Real Queries

The final week is about putting everything together. In real projects, window functions are rarely used in isolation. Queries often combine rankings, cumulative calculations, and partitions, which makes clarity and structure especially important.

This week, you focus on writing queries that use multiple window functions without becoming hard to read or maintain. You’ll learn how to mix ranking functions with running totals, how to apply different partitions in the same query, and how to avoid common mistakes such as incorrect ordering inside OVER() or unnecessary nesting.

You continue with the Window Functions course, completing Parts 8, 9, and 10, where more complex, real-world patterns are covered. The Window Functions Practice Set helps reinforce these patterns through hands-on repetition, while the SQL Window Functions Cheat Sheet becomes your everyday reference when writing window-function queries at work.

Who This Starter Pack Is For

This plan is ideal if you already know basic SQL and want to move into more analytical work. It fits data analysts, marketers, product specialists, and developers who work with reports or metrics.

If you are completely new to SQL, it’s better to start with SQL fundamentals before jumping into window functions.

Your January Plan in One Sentence

One advanced SQL skill, four focused weeks, and three proven LearnSQL.com resources.

Start with the Window Functions course, keep the cheat sheet open while learning, and use the practice set to lock everything in. By the end of January, window functions won’t feel advanced anymore – they’ll feel natural.