While basic SQL windows functions can be powerful, there are also more advanced techniques that can be used to gain even deeper insights from your data. Let’s take a look at some of these techniques.

### Partitioning

One technique for using windows functions is partitioning. Partitioning allows you to divide your result set into subsets based on a specific column or set of columns. This can be useful for calculating statistics within each subset.

For example, let’s say you have a table of sales data with columns for the date of the sale, the amount of the sale, and the salesperson who made the sale. You want to calculate the running total of sales for each salesperson, as well as the running total of sales for all salespeople combined. You can accomplish this using the PARTITION BY clause, like so:

`SELECT salesperson, date, amount, SUM(amount) OVER (PARTITION BY salesperson ORDER BY date) AS running_total_by_salesperson,`

SUM(amount) OVER (ORDER BY date) AS running_total_all_salespeople

FROM sales_data

In this example, the first window function calculates the running total of sales for each salesperson, partitioning the data by the salesperson column. The second window function calculates the running total of sales for all salespeople combined, with no partitioning.

### Window Frames

Another advanced technique for using windows functions is window frames. Window frames allow you to define the subset of rows over which the function should be applied.

For example, let’s say you have a table of sales data with columns for the date of the sale and the amount of the sale. You want to calculate the running total of sales for each day, but only include sales from the past week. You can accomplish this using the ROWS BETWEEN clause, like so:

`SELECT date, amount, SUM(amount) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS running_total_last_week`

FROM sales_data

In this example, the ROWS BETWEEN clause specifies that the window frame should include the current row and the six preceding rows, effectively limiting the running total to the past week.

### Nested Windows Functions

Finally, it’s worth noting that you can use nested windows functions to perform even more complex calculations. Nested windows functions allow you to apply a windows function to the result of another windows function.

For example, let’s say you have a table of sales data with columns for the date of the sale and the amount of the sale. You want to calculate the running total of sales for each day, but only include sales that are above the average sales for the entire dataset. You can accomplish this using a nested window function, like so:

`SELECT date, amount, AVG(amount) OVER () AS avg_amount,`

SUM(amount) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_total,

SUM(amount) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) / AVG(amount) OVER () AS sales_ratio

FROM sales_data

In this example, the first window function calculates the average sales for the entire dataset. The second window function calculates the running total of sales for each day. Finally, a third window function is used to divide the running total by the average sales, creating a ratio that only includes sales above the average.

## Conclusion

SQL windows functions are a powerful tool for working with data in SQL. By mastering the basics and exploring more advanced techniques like partitioning, window frames, and nested functions, you can gain deeper insights from your data and take your analysis to the next level. Remember to always test your queries on a representative dataset and consider performance considerations when working