orders +-------------+-------------+---------------+---------------------+ | delivery_id | customer_id | restaurant_id | order_place_time | +-------------+-------------+---------------+---------------------+ | 1001 | 1 | 1 | 2024-01-01 09:00:00 | | 1002 | 1 | 2 | 2024-01-02 09:01:00 | | ... | ... | ... | ... | +-------------+-------------+---------------+---------------------+ order_value +-------------+--------------+ | delivery_id | order_amount | +-------------+--------------+ | 1001 | 5.00 | | 1002 | 5.00 | | ... | ... | +-------------+--------------+ ##### Scenario You are a data scientist on the DoorDash marketplace team. You have an `orders` table (one row per delivery) and an `order_value` table with each delivery's amount. Work through the four parts below. ##### Question 1. **High-frequency orders by month.** A high-frequency customer places more than 30 orders in a calendar month. For each month, find the percentage of that month's orders placed by high-frequency customers. 2. **Top customer per month.** Excluding the high-frequency customers above, find the top customer for each month by total spend. If there is a tie, return every tied customer. 3. **Restaurant month-over-month sales.** Using `order_value` joined to `orders` (`restaurant_id`), compute the month-over-month change in total sales for `restaurant_id = 5`. Follow-up: generalize to every restaurant. 4. **Bottom-30% restaurant reach (read & rewrite).** Given a query that returns the percentage of customers who order from bottom-30%-by-sales restaurants, explain what it does, then rewrite it to compute that percentage per month. ##### Hints Think DATE_TRUNC for monthly buckets, COUNT per (month, customer) for the >30 filter, RANK() for ties, LAG() for month-over-month, and PERCENT_RANK() for the bottom-30% percentile.