Orders +-----------+-------------+------------------------+------------------------+ | order_id | customer_id | expected_delivery_date | actual_delivery_date | +-----------+-------------+------------------------+------------------------+ | 101 | 7 | 2023-05-01 | 2023-05-03 | | 102 | 12 | 2023-05-02 | 2023-05-02 | | 103 | 8 | 2023-05-03 | 2023-05-05 | | 104 | 15 | 2023-05-04 | 2023-05-04 | | 105 | 7 | 2023-05-05 | 2023-05-08 | +-----------+-------------+------------------------+------------------------+ ##### Scenario An e-commerce platform wants to monitor delivery performance over the past month. ##### Question Given a table Orders with expected_delivery_date and actual_delivery_date, write a SQL query that returns the percentage of orders delivered late in the last 30 days. Follow-up: List the top three customers with the highest number of late deliveries in that period. ##### Hints Filter by DATE_DIFF or > CURRENT_DATE-30, count late vs total, then GROUP BY customer_id and ORDER BY late_count DESC.