You are working with ads data. Assume the following tables, with all timestamps interpreted in UTC. - `advertisers(advertiser_id BIGINT, advertiser_category VARCHAR, created_at TIMESTAMP)` - `ads(ad_id BIGINT, advertiser_id BIGINT, creation_source VARCHAR, created_at TIMESTAMP)` - `ad_daily_stats(stat_date DATE, ad_id BIGINT, is_active BOOLEAN, spend_usd DECIMAL(18,2), impressions BIGINT, clicks BIGINT)` Relationships: - `ads.advertiser_id` references `advertisers.advertiser_id` - `ad_daily_stats.ad_id` references `ads.ad_id` Definitions: - An `active ad` on a given date is an ad with `is_active = TRUE` in `ad_daily_stats` for that `stat_date`. - `Last year` means `2023-01-01` through `2023-12-31`. - `This year` means `2024-01-01` through `2024-12-31`. - For part 1, define `pct_spend_in_2024` as `spend_2024 / (spend_2023 + spend_2024)`. - For part 2, report `month` as the first day of the calendar month, and define `share_of_monthly_spend` as `total_spend_usd / total included spend in that month`. Write SQL for the following tasks: 1. Find all advertisers whose total spend in 2023 exceeded 1000 USD. For each qualifying advertiser, return: - `advertiser_id` - `spend_2023` - `spend_2024` - `pct_spend_in_2024` 2. Investigate whether growth in one `creation_source` may have been driven by a decline in another source. Build a monthly report for 2023-2024 by `creation_source`, while excluding selected advertiser categories such as `Political`, `Government`, and `House Ads`. Return: - `month` - `creation_source` - `active_ads` - `distinct_advertisers` - `total_spend_usd` - `share_of_monthly_spend`