Window Functions in SQL for Better Data Analysis

Window Functions in SQL sound like a mouthful, don’t they? These handy tools can transform how you analyse data by allowing you to perform calculations across a set of table rows that are somehow related to your current row. Intrigued? You should be! Whether you’re a data enthusiast or a curious coder, understanding window functions can elevate your SQL skills to the next level. Stick around, and let’s unravel this powerful feature together!

What Are Window Functions in SQL?

Window functions in SQL are powerful SQL tools that perform calculations across a set of table rows that are somehow related to the current row. Unlike aggregate functions, which group multiple rows into one, window functions keep each row intact while adding calculated insights alongside.

These functions operate on a “window” or subset of rows defined by the OVER() clause, which can include optional PARTITION BY and ORDER BY statements to specify how the window should be framed.

Difference Between Aggregate Functions and Window Functions

Feature	Aggregate Functions	Window Functions
Row Retention	Return 1 row per group	Return 1 row per input row
Uses GROUP BY	Yes	No
Example	SUM(salary) GROUP BY dept	SUM(salary) OVER(PARTITION BY dept)
Common Usage	Total sales per region	Running total per user

In short: Aggregate functions collapse data; window functions enhance it without losing granularity.

Basic Syntax of Window Functions in SQL

<function_name>() OVER (
  [PARTITION BY column_name]
  [ORDER BY column_name]
)

Example:

RANK() OVER (PARTITION BY department ORDER BY salary DESC)

This would rank employees within each department based on their salary.

Why Use Window Functions in SQL for Data Analysis?

Real-World Scenarios for Window Functions

Window functions for SQL shine in analytical queries. Some common and practical use cases include:

• User Ranking – Assigning ranks to users based on points, purchases, or scores.
• Session Tracking – Identifying first/last activity within user sessions.
• Cumulative Stats – Calculating running totals or moving averages (e.g., daily sales).

Comparison with GROUP BY and Subqueries

Let’s compare how window functions in SQL stack up against traditional SQL methods like GROUP BY and subqueries.

Use Case	GROUP BY or Subquery Approach	Window Function Approach
Rank within groups	Complex nested subqueries	Simple with RANK()
Running totals	Requires self-joins or correlated subqueries	Easy with SUM() OVER(ORDER BY ...)
Previous/next row comparison	Not feasible or very complex	Use LAG() / LEAD() easily
Maintain original row context	Not possible with GROUP BY	Fully preserved

In essence, window functions in SQL reduce query complexity and enable advanced analytics directly within SQL, without the need for complex joins or temporary tables.

Why Use Window Functions for Data Analysis?

Window functions in SQL empower data analysts to extract deeper insights without losing row-level detail. Unlike traditional SQL methods, they let you analyze across a “window” of rows while keeping your results comprehensive and easy to read.

Real-World Scenarios Where Window Functions Shine

1. Ranking Users (RANK, DENSE_RANK, ROW_NUMBER)
   • Example: Ranking users based on purchase amount or scores.
   • Enables leaderboard-style reporting directly in SQL.
2. Session Tracking (LAG, LEAD, FIRST_VALUE, LAST_VALUE)
   • Identify the first and last login per user.
   • Compare values between rows (e.g., previous vs current login time).
3. Cumulative Stats (SUM, AVG, COUNT)
   • Running totals of daily sales or average spending per customer.
   • Monitor user behavior trends over time.

Comparison with GROUP BY and Subqueries

Feature / Task	GROUP BY / Subqueries	Window Functions
Maintain full row visibility	❌ Only grouped columns shown	✅ All row details retained
Row-by-row comparisons	❌ Complex with joins	✅ Easy with LAG(), LEAD(), FIRST_VALUE()
Ranking or sequential numbering	❌ Requires nested queries	✅ Built-in functions: RANK(), DENSE_RANK(), ROW_NUMBER()
Running totals / moving averages	❌ Requires self-joins	✅ Simple with SUM() OVER(ORDER BY ...)

In summary:
Window functions simplify advanced analysis and reduce code complexity, making them a preferred choice for modern SQL data workflows.

1. Understanding ROW_NUMBER()

The ROW_NUMBER() function is one of the most commonly used window functions. It assigns a unique sequential number to each row within the result set, based on the ORDER BY clause.

Syntax of ROW_NUMBER()

ROW_NUMBER() OVER (
  [PARTITION BY column_name]
  ORDER BY column_name
)

• PARTITION BY (optional): Splits the data into groups.
• ORDER BY: Defines the order in which the row numbers are assigned.

Simple Example with ORDER BY

SELECT employee_name, department, salary,
       ROW_NUMBER() OVER (ORDER BY salary DESC) AS row_num
FROM employees;

This assigns a unique row number to each employee, ordered by salary from highest to lowest.

Use Case: Pagination and Sequential Numbering

Use in Pagination:
Suppose you’re displaying 10 rows per page:

WITH RankedEmployees AS (
  SELECT *, ROW_NUMBER() OVER (ORDER BY employee_id) AS rn
  FROM employees
)
SELECT * FROM RankedEmployees
WHERE rn BETWEEN 11 AND 20;  -- page 2

Use in Data Cleaning or Deduplication:
You can identify and keep the first occurrence of duplicate records by assigning row numbers and filtering.

Use in UI Tables:
Show sequential row numbers in exported reports or HTML tables without changing actual data.

2. Using RANK() Function

The RANK() function is a window function used to assign a rank to each row within a partition of a result set. It orders the rows based on a specific column and assigns the same rank to rows with identical values (i.e., ties are allowed). However, it skips the next rank(s) after a tie.

How RANK Handles Ties

If two or more rows have the same value in the column used for ordering, they receive the same rank. But the next rank(s) are skipped.

For example, if two rows are ranked 1, the next rank will be 3 (not 2).

Example: Ranking Employees by Salary

SELECT employee_name, department, salary,
       RANK() OVER (ORDER BY salary DESC) AS rank
FROM employees;

Sample Output:

employee_name	salary	rank
Alice	80000	1
Bob	80000	1
Charlie	75000	3
David	70000	4

Notice how Charlie gets rank 3, not 2, because ranks 1 and 1 were assigned to Alice and Bob.

Use Case for RANK()

• Leaderboard-style rankings where tie positions should reflect gaps.
• Example: Sports tournament rankings, academic merit lists.

It’s best used when the ranking position matters, and gaps in rank numbers are acceptable or necessary for fairness.

3. Using DENSE_RANK() Function

The DENSE_RANK() function is similar to RANK() but does not skip any ranks after a tie. If two or more rows have the same value, they receive the same rank, and the next rank follows sequentially.

Difference Between RANK and DENSE_RANK

Function	Tie Behavior	Rank After Tie
RANK()	Same rank for ties	Skips next ranks
DENSE_RANK()	Same rank for ties	No skipped ranks

Code Comparison

SELECT employee_name, salary,
       RANK() OVER (ORDER BY salary DESC) AS rank,
       DENSE_RANK() OVER (ORDER BY salary DESC) AS dense_rank
FROM employees;

Sample Output:

employee_name	salary	RANK	DENSE_RANK
Alice	80000	1	1
Bob	80000	1	1
Charlie	75000	3	2
David	70000	4	3

• RANK() skips from 1 to 3 after a tie.
• DENSE_RANK() moves from 1 to 2, maintaining sequential integrity.

Best Use Case Scenarios for DENSE_RANK()

• Tiered Rankings: Loyalty levels, grading bands (Gold, Silver, Bronze).
• Score Grouping: Assigning prizes to participants with same scores.
• Pricing Levels: Grouping products into pricing tiers.

Use DENSE_RANK() when rank gaps may cause confusion or when you need continuous ranking without skips.

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Common Use Cases for These Functions

Window functions like ROW_NUMBER(), RANK(), and DENSE_RANK() are widely used in production SQL queries at data-driven companies across industries. These functions support real-time analytics, user-level ranking, and custom reports without needing complex joins or temporary tables.

Popular Companies Using Window Functions

Company	Domain	Use Case
Amazon	E-commerce	Ranking best-selling products per category
Netflix	Entertainment	Tracking user watch sessions and trends
Uber	Mobility	Ranking drivers by ride volume per region
Goldman Sachs	Finance	Calculating moving averages, volatility ranks
LinkedIn	Tech/HR	Ranking job applicants, engagement scores

Code Snippets & Use Cases

Example 1: Amazon – Top 3 Best-Selling Products Per Category

SELECT product_id, category, units_sold,
       RANK() OVER (PARTITION BY category ORDER BY units_sold DESC) AS sales_rank
FROM sales_data
WHERE sale_date >= CURRENT_DATE - INTERVAL '30 days';

Output:

product_id	category	units_sold	sales_rank
P101	Electronics	2100	1
P108	Electronics	1950	2
P119	Electronics	1800	3
P205	Books	1500	1

Amazon uses this to feature “Top 3” products in each category on category landing pages.

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Example 2: Netflix – Track First & Last Episodes Watched by a User

SELECT user_id, title,
       FIRST_VALUE(watch_time) OVER (PARTITION BY user_id ORDER BY watch_time) AS first_watched,
       LAST_VALUE(watch_time) OVER (
         PARTITION BY user_id ORDER BY watch_time 
         ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
       ) AS last_watched
FROM watch_history;

Helps Netflix generate personalized “Continue Watching” and “Your Watch History” features.

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Example 3: Uber – Rank Drivers by Rides per City

SELECT driver_id, city, COUNT(ride_id) AS total_rides,
       DENSE_RANK() OVER (PARTITION BY city ORDER BY COUNT(ride_id) DESC) AS ride_rank
FROM ride_data
GROUP BY driver_id, city;

Enables Uber to identify top-performing drivers in each city, useful for incentives.

Performance Tips & Best Practices

While window functions are powerful, improper use can lead to performance issues, especially on large datasets. Here are some optimization strategies:

1. Use Indexes Smartly

• Ensure the columns in the ORDER BY and PARTITION BY clauses are indexed.
• Example: If you frequently use ORDER BY created_at, index the created_at column.
• This reduces disk I/O and accelerates sorting.

2. Minimize the Window

Use filtering to reduce the dataset before applying the window function.

Do This:

WITH recent_orders AS (
  SELECT * FROM orders WHERE order_date > CURRENT_DATE - INTERVAL '90 days'
)
SELECT customer_id, order_value,
       ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_value DESC)
FROM recent_orders;

Not This:

SELECT customer_id, order_value,
       ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_value DESC)
FROM orders;  -- On entire table

3. Use PARTITION BY Only When Needed

If you don’t need group-wise ranking or calculations, omit PARTITION BY to avoid unnecessary grouping overhead.

4. Prefer ROW_NUMBER() for Pagination

Compared to RANK() or DENSE_RANK(), ROW_NUMBER() performs better in pagination because it guarantees unique row numbers, eliminating sorting ambiguities.

5. Benchmark with EXPLAIN PLAN

Use tools like EXPLAIN (PostgreSQL, MySQL) or Query Analyzer (SQL Server) to understand:

• Execution order
• Cost of sorting
• Index usage

6. Avoid Using SELECT *** in Production Queries

Explicitly mention only required columns, especially when dealing with window functions on large tables.

Conclusion

Window Functions in SQL enhance your ability to perform complex data analyses with ease and precision. By mastering them, you’ll feel empowered to tackle real-world datasets efficiently. Ready to upskill? Dive deeper into more programming languages like Java or Python with Newtum for further learning.

Edited and Compiled by

This article was compiled and edited by @rasikadeshpande, who has over 4 years of experience in writing. She’s passionate about helping beginners understand technical topics in a more interactive way.

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