This is a first approach to compute some easy metrics for the "deal" based business kpis. At this stage, it contains the information of bookings (created, checkout, cancelled) per deal and month, including both historic months as well as the current one. This do not contain MTD computation because it's overkill to do a MTD at deal level (+ we have 1k deals, so scalability can become a problem in the future)
Models:
- **int_dates_by_deal**: simple model that reads from **int_dates** and just joins it with **unified_users** to retrieve the deals. It will be used as the 'source of truth' for which deals should be considered in a given month, basically, since the first host associated to a deal is created (not necessarily booked)
- **int_core__monthly_booking_history_by_deal**: it contains the history of bookings per deal id in a monthly basis. It should be easy enough to integrate here, in the future and if needed, B2B macro segmentation.
In terms of performance, comparing the model **int_core__monthly_booking_history_by_deal** and **int_core__mtd_booking_metrics** you'll see that I removed the joined with the **int_dates_xxx** in the CTEs. This is because I want to avoid a double join of date & deal that I tried and I stopped after 5 min running. Since this computation is in a monthly basis - no MTD - it's easy enough to just apply the **int_dates_by_deal** on the last part of the query. With this approach, it runs in 7 seconds.
Related work items: #17689
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