You are analyzing a newly launched feed feature intended to improve engagement by showing more unconnected content. Assume the following tables: - `posts(post_id BIGINT, view_id BIGINT, relationship VARCHAR, post_date DATE)` - Each row represents one post impression in a user's feed. - `relationship` describes the relationship between the viewer and the post author. - Relevant values include `'friend'` and `'unconnected'`. - `interactions(interaction_id BIGINT, view_id BIGINT, interaction_type VARCHAR, interaction_ts TIMESTAMP)` - Each row represents one interaction on a feed impression. - `interaction_type` can be `'like'` or `'comment'`. - `view_id` joins to `posts.view_id`. Assume all dates and timestamps are in UTC. Interpret "last 7 days" as the inclusive window from `CURRENT_DATE - INTERVAL '6 day'` through `CURRENT_DATE`. Write SQL for the following: 1. Count how many feed impressions were for unconnected content in the last 7 days. - Output column: `unconnected_post_count` 2. For feed impressions in the last 7 days, compute the average number of reactions (`like` + `comment`) per impression for `friend` and `unconnected` content. - Include impressions with zero reactions. - Output columns: `relationship`, `avg_reactions_per_post`