Write the SQL query to extract specific data, using joins, aggregations, and filtering.
Use the provided customer, order, order item, and product tables. Return completed orders from the first quarter of 2025, retaining monthly category totals that reach at least 100 in revenue.
category, month, revenue, order_count, prior_month_revenue, and revenue_change_pct.category, then month ascending.| Column | Type | Description |
|---|---|---|
| customer_idPK | INT | Customer identifier |
| customer_name | VARCHAR(100) | Customer name |
| Column | Type | Description |
|---|---|---|
| order_idPK | INT | Order identifier |
| customer_id | INT | Customer who placed the order |
| order_date | DATE | Order date |
| status | VARCHAR(20) | Order status |
| Column | Type | Description |
|---|---|---|
| order_item_idPK | INT | Order line identifier |
| order_id | INT | Related order |
| product_id | INT | Related product |
| quantity | INT | Quantity purchased |
| unit_price | NUMERIC(10,2) | Price per unit at purchase |
| Column | Type | Description |
|---|---|---|
| product_idPK | INT | Product identifier |
| product_name | VARCHAR(100) | Product name |
| category | VARCHAR(50) | Product category |