Practice SQL with
real company scenarios.

Using a subquery in the FROM clause, first compute for each kitchen_name in swiggy_kitchen_orders: cancelled_count (orders with order_status = 'cancelled'), total_resolved (orders where order_status is not NULL), and cancellation_rate as cancelled_count divided by total_resolved times 100, rounded to 2 decimal places. Then, in the outer query, return only the kitchens where cancellation_rate is greater than 20, with columns kitchen_name, cancelled_count, total_resolved, and cancellation_rate, sorted by cancellation_rate in descending order.

For each employee, display their name, department, latest performance rating, the previous performance rating (to track improvement), their ranking within the department, and whether their latest rating is above or below their department's average. Sort by department and ranking.

For each user, count the total number of likes they have sent to others. Show user name along with the count, sorted by count in descending order.

For each rider, calculate the total number of trips they've taken and their average fare per trip. Treat any trip with a missing (NULL) fare_amount as 0 when calculating the average, rather than excluding it. Return rider_name, total_trips, and avg_fare (rounded to 2 decimal places), sorted by avg_fare in descending order.

Find total earnings of each driver and show top 3 earning drivers per city.

For each student in mock_test_scores who has a non-NULL score, calculate their percentile as PERCENT_RANK() expressed as a percentage (0 to 100, rounded to 2 decimal places) such that the highest-scoring student ends up with a percentile near 100 and the lowest-scoring student ends up near 0 — matching how 'percentile' is normally understood ('I scored better than X% of test-takers'). Exclude Vikram, whose score is NULL, entirely. Return student_name, score, and percentile, sorted by score descending.

For each video, calculate the percentage of viewers who watched until the end out of all viewers who started the video. Round the result to 2 decimal places.

For each store in zepto_daily_sales, excluding any row where revenue is NULL (the store was closed, not a zero-revenue day), compute a 3-day moving average of revenue ordered by sale_date — the average of the current trading day and the two trading days immediately before it (fewer days if not enough history exists yet). Return store_name, sale_date, revenue, and moving_avg_3day rounded to 2 decimal places, sorted by store_name and sale_date.

For each account in zerodha_clients where account_type is 'corporate', return client_name and email for accounts that either use a personal email domain (gmail.com, yahoo.com, or hotmail.com) or have no email on file at all. Sort by client_name.

Find every restaurant that has never had a cancelled order — meaning it has zero orders with status = 'cancelled'. This includes restaurants with no orders at all, since they trivially satisfy 'never cancelled.' Use EXISTS or NOT EXISTS with a correlated subquery rather than aggregating and filtering afterward. Return restaurant_name and city, sorted alphabetically by restaurant_name.