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sure! in this tutorial, we'll explore complex sql queries, focusing on various advanced concepts such as:

1. **joins**: combining data from multiple tables.
2. **subqueries**: queries within queries.
3. **common table expressions (ctes)**: named temporary result sets.
4. **window functions**: performing calculations across a set of table rows.
5. **aggregation and grouping**: summarizing data.

1. setting up the example database

let's assume we have a simple database for a bookstore with the following tables:

`authors`
`books`
`sales`

here’s how these tables might be structured:

*table: authors*
| author_id | name |
|-----------|---------------|
| 1 | author a |
| 2 | author b |
| 3 | author c |

*table: books*
| book_id | title | author_id | price |
|---------|-----------------|-----------|-------|
| 1 | book 1 | 1 | 15.00 |
| 2 | book 2 | 1 | 20.00 |
| 3 | book 3 | 2 | 25.00 |
| 4 | book 4 | 3 | 30.00 |

*table: sales*
| sale_id | book_id | quantity | sale_date |
|---------|---------|----------|-------------|
| 1 | 1 | 2 | 2023-01-01 |
| 2 | 2 | 1 | 2023-01-02 |
| 3 | 3 | 3 | 2023-01-01 |
| 4 | 4 | 1 | 2023-01-03 |

2. complex sql query examples

a. joining tables

let's write a query to get the total sales for each author.

```sql
select
a.name as author_name,
sum(b.price * s.quantity) as total_sales
from
authors a
join
books b on a.author_id = b.author_id
join
sales s on b.book_id = s.book_id
group by
a.name;
```

**explanation**:
we're joining `authors`, `books`, and `sales` using `join` on their respective ids.
we calculate total sales by multiplying the price of the book by the quantity sold.
finally, we group the results by author name.

b. using subqueries

now, let's find autho ...

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