# Description
This is a extremely simple but rather important PR.
It just sets the cutoff for KPIs reporting to April 2022. This affects 1) Main KPIs, 2) Guest KPIs and 3) Account Managers report
Motivation behind this is to have accurate data. Early 2022 might still be shitty, but at least we have a source of truth to compare against (on revenue side, finance P&L)
I set a dedicated variable because currency rates is reading from the same start date, and I intend only to modify KPIs cutoff.
# Checklist
- [X] The edited models and dependants run properly with production data.
- [NA] The edited models are sufficiently documented.
- [NA] The edited models contain PK tests, and I've ran and passed them.
- [NA] I have checked for DRY opportunities with other models and docs.
- [NA] I've picked the right materialization for the affected models.
# Other
- [ ] Check if a full-refresh is required after this PR is merged. **I need to manually run a full-refresh on daily listing segmentation that is incremental**
Adds dedicated start date for KPIs
Related work items: #26712
# Description
Adds `is_end_of_month_or_yesterday` for Main KPIs.
Apparently, the fact that we do not show the ongoing value of the month on some tabs of Main KPIs is the main blocker for Ben C to consistently use Main KPIs, which he actually retrieves from the SH legacy reporting. Since I'm sceptical about the data shown there, I want to remove this blocker.
It will require a small PR on PBI as well.
# Checklist
- [X] The edited models and dependants run properly with production data.
- [X] The edited models are sufficiently documented.
- [X] The edited models contain PK tests, and I've ran and passed them.
- [X] I have checked for DRY opportunities with other models and docs.
- [NA] I've picked the right materialization for the affected models.
# Other
- [ ] Check if a full-refresh is required after this PR is merged.
Related work items: #25342
# Description
Changes:
* Fixes weekly dates for KPIs. Before, joins were not working (but nothing was really used).
* Computes weekly new dash created services and exposes it to reporting
# Checklist
- [X] The edited models and dependants run properly with production data.
- [X] The edited models are sufficiently documented.
- [X] The edited models contain PK tests, and I've ran and passed them.
- [ ] I have checked for DRY opportunities with other models and docs.
- [X] I've picked the right materialization for the affected models.
# Other
- [ ] Check if a full-refresh is required after this PR is merged.
Related work items: #20809
# Description
Here I created a weekly aggregated model for guest journey metrics.
I changed the daily_dimension week start and end to iso so it matches the week date obtained by Postgres for this aggregation
# Checklist
- [x] The edited models and dependants run properly with production data.
- [x] The edited models are sufficiently documented.
- [x] The edited models contain PK tests, and I've ran and passed them.
- [x] I have checked for DRY opportunities with other models and docs.
- [x] I've picked the right materialization for the affected models.
# Other
- [ ] Check if a full-refresh is required after this PR is merged.
Related work items: #23373"
# Description
Creates skeleton for new KPIs data flow for created_bookings metric. Details are accessible [here](https://www.notion.so/knowyourguest-superhog/KPIs-Refactor-Let-s-go-daily-2024-10-23-1280446ff9c980dc87a3dc7453e95f06?pvs=4#12a0446ff9c98085bf4dfc77f6fc22f7)
In essence:
* Models are created in intermediate in a kpis folder.
* Models have a daily segmentation. This includes `created_bookings` models, but also the daily lifecycle per listing and the segmentation. It also adds a `dimension_dates` model specific for KPIs. These have all the dimensions already in place and handle all the crazy logic.
* Other time aggregation models simply read from existing daily models which are much easier (`int_kpis__metric_mtd_created_bookings` and `int_kpis__metric_monthly_created_bookings`).
* Dimensionality aggregation can be easily added within a given timeframe (daily, mtd, monthly). For instance, I do it for mtd in the `int_kpis__aggregated_mtd_created_bookings` and for monthly in `int_kpis__aggregated_monthly_created_bookings`
* Macro configuration for dimensions: Allows to set any specific dimension for `aggregated` models. By default, the subset of global, by billing country, by number of listings and by deal apply - since these are needed for Main KPIs. I added an example with Dash Source, that currently does not exist and it's currently configured as only appearing for created bookings.
* Testing `aggregated` models completeness. A new macro called `assert_dimension_completeness` is available that ensures additive metrics are consistent vs. the global result, configurable at schema level.
* Testing refactor impact. I'm aware that changing the lifecycle model to daily impacts the volumes for listing segments. For the rest, I added a `tmp` test that checks that the dimension and dimension value per date exactly match comparing new vs. old computation.
Latest edits:
* Changed naming convention
* Split of MTD and Monthly. Now these are 2 different entities, as stated in `int_kpis__dimension_dates`.
* Added start_date and end_date for models that contemplate a range (mtd, monthly).
* Added a small readme entry in the kpis folders. Mostly it states nomenclature and some first conventions.
Dbt docs:
# Checklist
- [X] The edited models and dependants run properly with production data.
- [X] The edited models are sufficiently documented.
- [X] The edited models contain PK tests, and I've ran and passed them.
- [ ] I have checked for DRY opportunities with other models and docs. **Likely we'll be able to add macros for mtd and dim_agg models. We will see later on.**
- [ ] I've picked the right materialization for the affected models. **Models run ok except for the daily lifecycle of listings, which lasts several minutes in the first run. Model curr...
