events +----------+---------+---------------------+ | event_id | user_id | event_time | +----------+---------+---------------------+ | 1 | 101 | 2023-01-01 09:00:00 | | 2 | 101 | 2023-01-01 10:30:00 | | 3 | 102 | 2023-01-02 08:15:00 | | 4 | 103 | 2023-01-03 12:45:00 | +----------+---------+---------------------+ ​ sessions +-----------+---------+---------------------+---------------------+ | session_id| user_id | session_start | session_end | +-----------+---------+---------------------+---------------------+ | 1001 | 101 | 2023-01-01 08:45:00 | 2023-01-01 10:45:00 | | 1002 | 102 | 2023-01-02 08:00:00 | 2023-01-02 09:00:00 | | 1003 | 103 | 2023-01-03 08:30:00 | 2023-01-03 13:30:00 | +-----------+---------+---------------------+---------------------+ ##### Scenario You are a data analyst exploring product-usage logs and session metadata to understand user engagement. ##### Question Q1. Using table events, write a SQL query that returns the total number of events generated by each user (user_id). Q2. Using table events, return for every user_id the earliest event_time and the latest event_time observed. Q3. Using tables events and sessions, write a SQL query that finds the user_id whose single session has the longest duration (session_end – session_start); return both the user_id and that duration. ##### Hints Window functions, GROUP BY, TIMESTAMPDIFF; join events e ON e.user_id = s.user_id when needed.