Converting rows into columns is often referred to as "pivoting." In SQL, you can achieve this using the *CASE* statement in conjunction with an aggregate function, typically *SUM()* or *COUNT()*.
Here's a generic way to convert rows into columns using the *CASE* statement:
###### Example Scenario

Suppose you have a table called *Sales* with the following structure:
| ID | Product | Amount |
|----|-----------|--------|
| 1 | A | 100 |
| 2 | B | 200 |
| 3 | A | 150 |
| 4 | B | 250 |
| 5 | C | 300 |
###### Goal

You want to transform this table so that each product becomes a column, and the rows are aggregated by the *Amount*.
###### SQL Query

Here’s how you’d write a SQL query to achieve that with the *CASE* statement:
SELECT
SUM(CASE WHEN Product = 'A' THEN Amount ELSE 0 END) AS Product_A,
SUM(CASE WHEN Product = 'B' THEN Amount ELSE 0 END) AS Product_B,
SUM(CASE WHEN Product = 'C' THEN Amount ELSE 0 END) AS Product_C
FROM
Sales;

###### Explanation

1. **CASE Statement**: For each product, we use a *CASE* statement to assign the *Amount* to its respective product column. If the condition (*Product = 'A'*, *Product = 'B'*, etc.) is true, it returns the *Amount*. Otherwise, it returns 0.
2. **SUM Function**: The *SUM* function is used to aggregate the results, ensuring that all amounts for each product are summed together.
3. **Column Aliasing**: We alias each sum with a meaningful column name (*Product_A*, *Product_B*, etc.) for clarity in the result set.
###### Result

The output of this query will be:
| Product_A | Product_B | Product_C |
|-----------|-----------|-----------|
| 250 | 450 | 300 |
In this result:
- *250* is the total for Product A (100 + 150).
- *450* is the total for Product B (200 + 250).
- *300* is the total for Product C.
###### Additional Notes

- If you have more products, you'll need to extend the *CASE* statements accordingly.
- It's important to consider performance for large datasets, as using multiple *CASE* statements can become cumbersome. In such cases, consider using more advanced techniques like Common Table Expressions (CTEs) or specific pivot functions if supported by your SQL dialect.