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Data Analyst
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Prompt Engineer
The Go-to-Market team at Amazon came back to you and felt that the list was probably too short. They need to know the top 10% on the platform for each country.
Pull that list of these top spenders including their name, country and total_spend.
users table:
| Column Name | Description |
|---|---|
| user_id | Unique identifier for each user. |
| name | The name of the user |
orders table:
| Column Name | Description |
|---|
| order_id | Unique identifier for each order. |
| order_amount | The total amount of the order |
| buyer_id | The user_id who purchased the order |
| name | country | total_spend |
|---|---|---|
| Nathan Roy | Canada | 376.35 |
| Michael Girard | Canada | 366.50 |
| Oliver Dupuis | US | 301.65 |
| Gabriela Ramírez | Mexico | 233.40 |
The Go-to-Market team at Amazon came back to you and felt that the list was probably too short. They need to know the top 10% on the platform for each country.
Pull that list of these top spenders including their name, country and total_spend.
users table:
| Column Name | Description |
|---|---|
| user_id | Unique identifier for each user. |
| name | The name of the user |
orders table:
| Column Name | Description |
|---|---|
| order_id | Unique identifier for each order. |
| order_amount | The total amount of the order |
| buyer_id | The user_id who purchased the order |
| name | country | total_spend |
|---|---|---|
| Nathan Roy | Canada | 376.35 |
| Michael Girard | Canada | 366.50 |
| Oliver Dupuis | US | 301.65 |
| Gabriela Ramírez | Mexico | 233.40 |