ROW_NUMBER, RANK, DENSE_RANK - Master ranking functions for pagination and top-N queries

PARTITION BY vs GROUP BY - Deep dive into when to use each approach

Window Frame Specifications - Control exactly which rows are included in calculations

Running Totals & Moving Averages - Time-based analytics patterns

LEAD, LAG, FIRST_VALUE, LAST_VALUE - Access other rows for comparisons

Calculate each employee's salary vs their department average

Create running totals of sales by date

Rank products by sales within each category

Show each order with customer's total order count

Compare monthly revenue to previous month using LAG

Find top 3 performers in each department using window functions

Calculate moving 7-day average of website traffic

Show each student's grade percentile within their class

Explain the difference between window functions and GROUP BY with examples

What does the OVER clause do and what are its components?

How do you optimize window function performance?

When would you use window functions instead of correlated subqueries?

Write a query showing running totals and department rankings

How do you handle the "cannot use window function in WHERE clause" error?

What are the default window frame specifications?

Explain PARTITION BY vs GROUP BY in terms of result set size

PostgreSQL Window Functions Documentation - Comprehensive reference

SQL Server Window Functions Guide - Microsoft's detailed examples

MySQL 8.0 Window Functions - New features and syntax

Window Functions Performance Tuning - Optimization strategies

Advanced Window Function Patterns - Real-world use cases

SQL Analytics with Window Functions - Business intelligence applications

You now understand window functions fundamentals - the foundation of modern SQL analytics. You can use the OVER clause to perform calculations while preserving all rows, understand the difference from GROUP BY, and apply basic PARTITION BY and ORDER BY concepts.

Try the basic window function examples in our SQL editor

Experiment with PARTITION BY on different columns

Compare window function results with GROUP BY equivalents

Practice combining multiple window functions in one query

Test performance differences with and without proper indexes

Next, you'll dive deeper into specific window function types: ranking functions (ROW_NUMBER, RANK, DENSE_RANK) for pagination and leaderboards, then offset functions (LAG, LEAD) for time-series analysis, and finally advanced frame specifications for precise moving calculations.