shops +---------+------------+---------------+ | shop_id | created_at | category | +---------+------------+---------------+ | 1 | 2024-01-03 | fashion | | 2 | 2023-11-20 | home | | 3 | 2024-02-10 | electronics | | 4 | 2023-09-15 | fashion | | 5 | 2024-03-01 | toys | +---------+------------+---------------+ ​ page_views +----------+---------+---------------------+ | shop_id | user_id | viewed_at | +----------+---------+---------------------+ | 1 | 501 | 2024-03-10 09:01:15 | | 2 | 877 | 2024-03-10 09:05:11 | | 1 | 612 | 2024-03-10 09:06:55 | | 3 | 501 | 2024-03-10 09:07:15 | | 4 | 933 | 2024-03-10 09:09:05 | +----------+---------+---------------------+ ##### Scenario Marketplace wants to understand whether newly onboarded shops are more active and visible than existing ones. ##### Question Given the tables below, write SQL to compute daily shop visibility (number of page_views per shop). Then propose and calculate a metric that demonstrates whether shops created in the last 30 days are more active than older shops. ##### Hints Join page_views with shops, group by shop_id and date, compare average views of new-shop cohort (<30 days) versus older cohort; consider median to limit outlier impact.