# Description
Quick improvement to be able to report the same setup of metrics by considering all account history up to a certain date.
It adds a new computation flow for this All History window. Note that I needed to update the macro to override this case by using unbounded preceding.
I also took the opportunity to update the exposures of the new report.
# 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: #26085
# Description
This model aggregates at monthly by deal level different metrics for AM reporting purposes. It also includes revenue retained ratios for client profitability assessment.
There's part of the existing AM report that could be simplified, likely, by using the new macro. This will be explored in a separated PR, if it applies.
# 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. ** Checked and there might be possibilities to simplify the code. I'll check ones I finish this line of work**
- [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: #25829
# Description
Creates the models for KPIs for New Dash - Chargeable metrics. In essence, computes 4 metrics:
- Chargeable Services
- Chargeable Amount (in GBP)
- Chargeable Bookings (unique over a time period and dimension, not additive)
- Chargeable Listings (unique over a time period and dimension, not additive)
This is done by creating:
- A Weekly Metric and Monthly Metric model. Here we keep the granularity of id_booking / id_accommodation to be able to compute the uniqueness.
- A Daily, Weekly and Monthly Aggregated models. Same as usual.
- Integrates everything to the existing model for Product New Dash Agg Metrics
- Exposes everything into reporting
NB: I removed on "by_host" in Created Services - I forgot to clear it out.
# 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: #20809
# Description
This PR has 2 commits:
- The first one handles the removal from the computation any user that has not an id deal properly set. I just created a boolean field in int_core__user_host that identifies if the host has no id_deal. Then apply the new condition in the 2 main usages of New Dash info.
- The second one cleans New Dash KPIs. Since we do not have anymore users without deals, it means that the identification of the host/account is going to be exactly the same if done by id_user_host or id_deal. I hated having id_user_host in KPIs so I've removed it :)
Lastly, this should speed up massively the execution. Not because there's improvements on the code but rather the reduction of data.
# 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: #20809
# Description
Creates a macro for beautification of categorical values.
# 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: #20809
# Description
This PR handles the computation of KPIs for New Dash, focusing on Created Services.
New dimensions configured in `business_kpis_configuration` and applied in this new models for `NEW_DASH_CREATED_SERVICES`:
* `dim_host`,
* `dim_has_upgraded_service`,
* `dim_new_dash_version`,
* `dim_pricing_service`
New daily metric model `int_kpis__metric_daily_new_dash_created_services`
* Follows a similar pattern as for the rest of daily metric models. The only difference is that is aggregated to `id_booking` to ensure we can handle count distinct of bookings per different time granularities.
* Reads from the new pricing tables `int_core__booking_summary` and `int_core__booking_service_detail`. The main filters applied are selecting only new dash users and only services created after the user move timestamp to new dash.
An additional metric model at monthly level is created `int_kpis__metric_monthly_new_dash_created_services`
These finally go to a dimension aggregated model (`dimension`, `dimension_value`), respectively:
* Daily: `int_kpis__agg_daily_new_dash_created_services`
* Monthly: `int_kpis__agg_monthly_new_dash_created_services`
A final model aims to aggregate the different dimension aggregated metrics for New Dash: `int_kpis__product_new_dash_agg_metrics`
* It computes a `time_granularity` aggregation
* Here I will add additional metrics (such as revenue) once we have them.
A final model reading from the previous is exposed to reporting: `kpis__product_new_dash_agg_metrics`
# 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: #20809
# Description
New model:
* int_kpis__agg_dates_main_kpis - Serves as the skeleton of dates and dimensions for Main KPIs. It's aggregated since it follows a similar aggregation strategy. It's a single model to feed both Main KPIs visualisations. Note boolean fields are real booleans (true/false) while before these were integers (1/0). This also affects downstream models.
Main KPIs flow adaptations to new skeleton model:
* int_monthly_aggregated_metrics_history_by_deal
* int_monthly_churn_metrics - additionally, calls usual KPIs macro instead of old one
* int_mtd_vs_previous_year_metrics
Reporting changes to ensure report is not down:
* mtd_aggregated_metrics - adaptations on booleans (true-1, false-0)
Cleaning:
* get_kpi_dimensions macro is no longer used
* int_dates_by_deal model and schema entry
* int_dates_mtd_by_dimension model and schema entry
* int_dates_mtd model and schema entry
# 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: #23763
# Description
Changes:
* Creates lifecycle_daily_deal, metric_daily_deals and agg_daily_deals. These follow a different strategy due to the nature of the metrics
* Modifies the dimension macro to ensure deal dimension is included in all models except these ones
* Fixes production issue on currently deployed deal lifecycle and metrics
# 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: #23566
# Description
Considers the 2 Claims NewDashVersion and NewDashMoveDate as sources of truth, removing the previous (crazy) logic.
If a user has the claim NewDashVersion, then it's in New Dash. The claim value will also provide the version in which the user appeared (MVP, V2, etc)
If a user has the NewDashMoveDate, it means it has moved from Old Dash. If not, but still has NewDashVersion, it means the user was directly created in New Dash.
The models now provide logic to handle these cases, and it's propagated downstream will ensuring reporting will still work.
# Checklist
- [X] The edited models and dependants run properly with production data.
- [X] The edited models are sufficiently documented.
- [NOT AT ALL] The edited models contain PK tests, and I've ran and passed them. **MANY ISSUES ON PRODUCTION CURRENTLY**
- [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.
Changes claims and logic to consider user is in new dash. Downstream propagation included
Related work items: #23457
# 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...
# Description
This PR integrates screening API verifications into staging.
There's 2 commits:
* The earliest one, it just copy-pastes the strategy followed by edeposit and adapts it to fit screening API case, which is simpler. Note here that the schema entries contain a low number of tests. This is because we only have 7 records in production - and these seem fake anyway :) - so it's complex to extrapolate. Those I could extrapolate (NoFlags/Flagged) I'm taking from the data transformation within the PBI report.
* The last commit, it's just a DRY. It handles the deduplication logic for cosmos db in a macro, and I applied it on both the screening API and the edeposit verifications. Works well from my POV.
Since you guys are far more knowledgeable on APIs scope, I'll let you take a close look in case I missed something.
# Checklist
- [X] The edited models and dependants run properly with production data.
- [ ] The edited models are sufficiently documented. **I guess it can be improved, waiting for your comments here**
- [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.
- [ ] I've picked the right materialization for the affected models. **Used default coming from stg_edeposit__verifications**
# Other
- [ ] Check if a full-refresh is required after this PR is merged.
Related work items: #20127
# Description
Prepares setup to automatically detect V2 migrated users from old dash to new dash by using the new claim type KygMoveDate.
# Checklist
- [X] The edited models and dependants run properly with production data. **Works well with current setup but there's no migrated users yet. We'll need to double check once the launch date has happened**
- [ ] The edited models are sufficiently documented. **N/A**
- [ ] The edited models contain PK tests, and I've ran and passed them. **N/A**
- [ ] I have checked for DRY opportunities with other models and docs. **N/A**
- [ ] I've picked the right materialization for the affected models. **N/A**
# Other
- [ ] Check if a full-refresh is required after this PR is merged.
Related work items: #20810
# Description
Fixing logic to ensure priority selection of claims when user satisfies multiple claim conditions.
It adds a new parameter that forcefully prioritises the selection of the date value for a certain claim over the others. If the value is repeated among claims, it will select the earliest date.
# 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: #20773
# Description
This PR fixes the New Dash migration issue that happened on September 10th 2024. In this migration, users were directly assigned the claim of KygMvp that does not contain a date value. We were using a default hardcode of the first MVP migration, thus in DWH all users have been considered to be migrated late July instead of splitting the first 22 in late July and the ~200 others in September.
The issue lies in the fact that users have configured a ProductBundle and can have Bookings with ProductBundle BEFORE the migration date, which greatly breaks the logic of a migration monitoring.
Changes:
* New migration phase added based on the claim MvpMigratedUser, that Ben created on Friday 13th
* Adaptation of the code in int_core__user_migration to detect if the claim_value (a text field) has a date or not. If so, use that date as long as it's equal or greater than the deployment date, if not use the deployment date. If the claim does not contain a date, use the deployment date (this is the case for the first true 22 migrated users)
I checked that volumes now look correct with this fix.
# 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: #20773
# Description
Before deploying KPIs by Billing Country, we spotted some issues that were basically increases on the volumes of any metric on the by billing country dimension that was based on Deal. This means, `int_core__mtd_deal_metrics` and `int_xero__mtd_invoicing_metrics`.
This PR changes the following:
* Now the 2 abovementioned models depend on the `int_core__deal` model, instead of `int_core__user_host` (thus removing duplicated stuff)
* Now all models use the main billing country at deal level, instead of doing it so at host level. The reason is that some small amount of hosts that share the same deal can have a different billing country. To avoid weird stuff, everything points to this simplification - that in general, it's not a massive change in the output.
* In order to do so easily, the 3 main billing country per deal fields have been propagated to `int_core__user_host`
To exemplify the solution, find here a snapshot of the differences in behavior:
```
select
dimension,
sum(deals_booked_in_month) as deals_booked_1,
sum(deals_booked_in_6_months) as deals_booked_6,
sum(deals_booked_in_12_months) as deals_booked_12,
sum(total_revenue_in_gbp) as total_revenue,
sum(xero_operator_net_fees_in_gbp) as operator_revenue,
sum(xero_booking_net_fees_in_gbp) as booking_fees,
sum(xero_listing_net_fees_in_gbp) as listing_fees,
sum(xero_verification_net_fees_in_gbp) as verification_fees,
sum(total_guest_revenue_in_gbp) as guest_revenue,
sum(xero_waiver_paid_back_to_host_in_gbp) as waiver_paid_back_to_hosts,
sum(waiver_net_fees_in_gbp) as waiver_net_fees
from intermediate.int_mtd_vs_previous_year_metrics
where date in ('2024-01-31')
group by 1
order by 1
```
Production:
vs.
Local:
Keep in mind that still Global dimension can be greater than any other dimension aggregated since not all users have a deal. Mismatches between the other 2 dimensions might be linked to the dump.
Commits are meaningful and help navigate in the changes.
# 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: #20823
# Description
Adds Billing Country dimension in KPIs, but does not expose them to reporting yet.
Silly thing, based on the macros I built, I cannot make incremental changes unless changing all models. This will need to be adapted, happy to hear your thoughts on how we do it.
Additionally, I have lack of performance of the model `mtd_guest_payments_metrics`. It takes around 5 min to execute, but technically the end-to-end runs in one shoot without breaking.
It's a complex PR because it changes many files, but you will see that:
* It mostly changes the join conditions for the dimensions or the schema tests,
* I tried to be very careful and add things step-by-step in the commits.
Goal is NOT to complete the PR yet until we see how we can improve performance. I can say though that data end-to-end looks ok to me, but would benefit from checking with production data for the new dimension
Update 30th Aug
* Added a new commit that includes `id_user_host` in `int_core__verification_payments`. Happy to discuss if it makes sense or not. But it changes the execution from ~600 sec to ~6 sec because it avoids a massive repeated join with `verification_requests`.
# 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.
- [ ] I've picked the right materialization for the affected models. **To check because of performance issues**
# Other
- [ ] Check if a full-refresh is required after this PR is merged.
Related work items: #19082
# Description
First step towards reporting New Dash is to be able within DWH to know which hosts have been migrated.
In order to do so, and anticipating that there's going to be new phases in the future, I've created a `int_core__user_migration` model that reads from a configuration macro `get_new_dash_migration_phases_config` that will allow semi-automatic user retrieval in the future. This avoids nasty hardcoding within the model itself.
The information of whether a user is migrated, in which phase and when the phase was deployed is available at user level in the `int_core__user_host` table.
# 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. **-> I selected a view for this model since I don't believe we should materialse this data other than the user host table**
# Other
- [ ] Check if a full-refresh is required after this PR is merged.
Related work items: #19570
# Description
Sets the parameter to display the KPIs by number of listings to prod. I will move forward without the review as we need a simultaneous deployment. The combination of changes were reviewed yesterday in local.
# Checklist
- [ ] The edited models and dependants run properly with production data.
- [ ] The edited models are sufficiently documented.
- [ ] The edited models contain PK tests, and I've ran and passed them.
- [ ] I have checked for DRY opportunities with other models and docs.
- [ ] 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: #19325
# Description
Changes:
* Separate 1) the internal naming of dimensions available within DWH vs. 2) the display of the dimensions in the reporting. Mainly it changes the "by_number_of_listings" to display "By # of Listings Booked in 12 Months". I edited the production macro since to me it's linked to when things are available for display.
* Add preceding zeros on the segmentation so it's ordered correctly. Before, the segment 21-60 was displayed before the 6-20.
* Also added some capital letters to the schema config of the reporting model :)
I attach a screenshot of how it looks in PBI in my local development branch to exemplify why this is "Beautification". Be aware that merging this also puts in production the dimensions.
# 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: #19325
# Description
Takes into account @<Pablo Martín> 's feedback from the previous PR, slightly modified. This PR separates 1) the dimensions while developing vs. 2) the dimensions once these are available for production. This are within the same file of macro configuration for KPIs, namely `business_kpis_configuration`.
End-goal, all CTEs in `int_mtd_vs_previous_year_metrics` will read from this new macro `get_kpi_dimensions_for_production`, so eventually we won't need any hardcode once we want to add new dimensions. In the meantime, I'll be adding this new line for each PR (still 2 missing :D)
# Checklist
- [X] The edited models and dependants run properly with production data.
- [X] The edited models are sufficiently documented.
- [ ] 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.
- [ ] 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: #19325
