🚀 5 Essential SQL Window Function Use Cases for Data Analysts in 2025! #SQL #DataAnalysis #Window

Window functions enable powerful, in-place analytics without complex self-joins or subqueries. Below are five practical use cases EVERY data analyst should master in 2025, along with links to tools and tutorials to deepen your expertise:

🔹 1. Ranking Records with ROW_NUMBER()

Assign sequential integers to rows within partitions—useful for pagination or picking the “top N” per group.

SELECT
customer_id,
order_date,
ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date DESC) AS rn
FROM orders;

🔹 2. Handling Ties Using RANK() & DENSE_RANK()

RANK() gives the same rank to tied values but skips subsequent ranks; DENSE_RANK() doesn’t skip. Ideal for leaderboard-style reports.

SELECT
product,
sales,
RANK() OVER (ORDER BY sales DESC) AS rank,
DENSE_RANK() OVER (ORDER BY sales DESC) AS dense_rank
FROM product_sales;

🔹 3. Calculating Moving Averages with AVG() OVER

Smooth out time series data by computing moving averages over a sliding window.

SELECT
date,
revenue,
AVG(revenue) OVER(ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS moving_avg
FROM daily_revenue;

🔹 4. Comparing Rows Using LAG() & LEAD()

Access values in preceding or following rows to calculate differences or detect changes.

SELECT
date,
revenue,
revenue - LAG(revenue,1) OVER(ORDER BY date) AS diff
FROM daily_revenue;

🔹 5. Distributing Data into Buckets with NTILE()

Divide rows into a specified number of roughly equal groups for percentile analysis.

SELECT
employee,
salary,
NTILE(4) OVER(ORDER BY salary) AS quartile
FROM employee_salaries;

📌 Pro Tip:

Leverage your database’s built-in query profiler (e.g., PostgreSQL’s EXPLAIN ANALYZE) when using window functions on large datasets to ensure optimal performance.