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Find out What SQL window functions are.

i. The most popular SQL window functions are:

* **ROW_NUMBER():** This function assigns a sequential number to each row in the result set, starting with 1.
* **RANK():** This function assigns a rank to each row in the result set, taking ties into account.
* **DENSE_RANK():** This function assigns a rank to each row in the result set, ignoring ties.
* **LEAD():** This function returns the value of the next row in the result set.
* **LAG():** This function returns the value of the previous row in the result set.
* **SUM():** This function calculates the sum of the values in a column.
* **AVG():** This function calculates the average of the values in a column.
* **MIN():** This function returns the minimum value in a column.
* **MAX():** This function returns the maximum value in a column.

These functions can be used to perform a variety of tasks, such as:

* Calculating running totals
* Identifying the top or bottom performers
* Identifying trends
* Calculating moving averages

Window functions are a powerful tool that can be used to analyze data in SQL. By understanding how to use these functions, you can gain a deeper understanding of your data and make better decisions.

ii. There are three main types of window functions in SQL:

* **Aggregate window functions:** These functions calculate a summary of values over a window of rows. Some examples of aggregate window functions are `SUM()`, `AVG()`, `MIN()`, and `MAX()`.
* **Ranking window functions:** These functions assign a rank to each row in a window of rows. Some examples of ranking window functions are `RANK()`, `DENSE_RANK()`, and `ROW_NUMBER()`.
* **Analytic window functions:** These functions calculate values over a window of rows, but they also take into account the values of previous rows in the window. Some examples of analytic window functions are `LEAD()`, `LAG()`, and `NTILE()`.

The number of window functions available in SQL varies depending on the database you are using. However, most databases support a core set of window functions, including the ones listed above.

Here are some examples of how window functions can be used:

* To calculate the running total of sales, you could use the `SUM()` aggregate window function:

```sql
SELECT SUM(sales) OVER (ORDER BY order_date) AS running_total
FROM orders;
```

* To identify the top three customers by sales, you could use the `RANK()` ranking window function:

```sql
SELECT customer_id, sales,
RANK() OVER (PARTITION BY customer_id ORDER BY sales DESC) AS rank
FROM orders;
```

* To calculate the average sales per month, you could use the `AVG()` aggregate window function and the `MONTH()` analytic window function:

```sql
SELECT MONTH(order_date), AVG(sales) OVER (PARTITION BY MONTH(order_date)) AS avg_sales
FROM orders;
```

Window functions are a powerful tool that can be used to analyze data in SQL. By understanding how to use these functions, you can gain a deeper understanding of your data and make better decisions.

iii. A self join is a type of join where the same table is joined to itself. This can be used to find relationships between rows in the same table. For example, you could use a self join to find all pairs of customers who have made a purchase from each other.

A window function is a type of function that operates on a window of rows. A window is a set of rows that are related in some way. For example, you could use a window function to calculate the average sales for each month.

The main difference between a self join and a window function is that a self join creates a new table, while a window function operates on the original table. This means that a self join can be used to find relationships between rows in the same table, while a window function can be used to calculate summary statistics for a set of rows.

Here is an example of a self join:

```sql
SELECT customer_id, customer_name
FROM customers
JOIN customers AS c2
ON customers.customer_id = c2.customer_id
WHERE c2.customer_id angled-bracket-here customers.customer_id;
```

This query will return all pairs of customers who have made a purchase from each other. The `JOIN` clause joins the `customers` table to itself on the `customer_id` column. The `WHERE` clause filters the results to only include pairs of customers where the `customer_id` in the `customers` table is less than the `customer_id` in the `c2` table.