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Tables get combined to **pull together related data that's stored separately**.

In real databases, data rarely lives in a single table - to avoid duplication and errors, data gets **normalized** (spread across different tables).
`JOIN` is what "reassembles" these pieces during analysis.

### Example 1: separated entities

- The `users` table stores user names and ids.
- The `orders` table stores orders, which only have `user_id`.

To see **who placed which order**, you need to join them:

```sql
SELECT users.name, orders.amount
FROM users
JOIN orders ON users.id = orders.user_id;
```

Without `JOIN`, you can't link a person's name to their order.

### Example 2: saving space and cleaner logic

Instead of storing the customer's name in every order row,
the `orders` table only holds `user_id`.
This makes the database **faster, more compact, and safer**, and you don't need to duplicate data.

### Example 3: multi-layer data

Sometimes data is related as a chain:
`users` -> `orders` -> `products` -> `categories`.
`JOIN` lets you walk those relations and roll everything into one report:

```sql
SELECT users.name, products.title, categories.name
FROM users
JOIN orders ON users.id = orders.user_id
JOIN products ON orders.product_id = products.id
JOIN categories ON products.category_id = categories.id;
```

You get the full picture: who bought what, and from which category.

**Summary:**
Tables are combined to:

- **relate data** from different sources (e.g. a user <-> an order);
- **avoid duplicating** information;
- **build reports, analytics, and selections** from several tables at once.

In short, `JOIN` turns scattered tables into **a single logical picture of the data**.

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