Revert cross models
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8 changed files with 18 additions and 18 deletions
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@ -1,7 +1,7 @@
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{{ config(materialized="table", unique_key=["date", "id_deal"]) }}
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with
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int_monthly_agg_metrics_history_by_deal as (
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select * from {{ ref("int_monthly_agg_metrics_history_by_deal") }}
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int_monthly_aggregated_metrics_history_by_deal as (
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select * from {{ ref("int_monthly_aggregated_metrics_history_by_deal") }}
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),
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agg_metrics_by_deal_over_12_months as (
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select
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@ -28,7 +28,7 @@ with
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order by am.date
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rows between 12 preceding and 1 preceding
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) as sum_listings_booked_in_month_preceding_12_months
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from int_monthly_agg_metrics_history_by_deal am
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from int_monthly_aggregated_metrics_history_by_deal am
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),
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global_computation_over_12_months as (
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select
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@ -1,7 +1,7 @@
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{{ config(materialized="table", unique_key=["date", "id_deal"]) }}
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with
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int_monthly_agg_metrics_history_by_deal as (
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select * from {{ ref("int_monthly_agg_metrics_history_by_deal") }}
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int_monthly_aggregated_metrics_history_by_deal as (
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select * from {{ ref("int_monthly_aggregated_metrics_history_by_deal") }}
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),
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int_hubspot__deal as (select * from {{ ref("int_hubspot__deal") }}),
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deal_history_from_previous_months as (
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@ -17,7 +17,7 @@ with
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coalesce(total_revenue_in_gbp, 0) as revenue_in_gbp,
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coalesce(created_bookings, 0) as created_bookings,
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coalesce(listings_booked_in_month, 0) as listings_booked_in_month
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from int_monthly_agg_metrics_history_by_deal am
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from int_monthly_aggregated_metrics_history_by_deal am
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-- Do not show data of ongoing month
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where am.date < date_trunc('month', current_date)::date
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),
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@ -113,8 +113,8 @@ exposures:
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Additionally, it allows to retrieve similar KPIs at Deal level.
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depends_on:
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- ref('mtd_agg_metrics')
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- ref('monthly_agg_metrics_history_by_deal')
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- ref('mtd_aggregated_metrics')
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- ref('monthly_aggregated_metrics_history_by_deal')
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owner:
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name: Oriol Roqué
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@ -1,6 +1,6 @@
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with
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int_monthly_agg_metrics_history_by_deal as (
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select * from {{ ref("int_monthly_agg_metrics_history_by_deal") }}
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int_monthly_aggregated_metrics_history_by_deal as (
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select * from {{ ref("int_monthly_aggregated_metrics_history_by_deal") }}
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)
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select
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@ -113,4 +113,4 @@ select
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deposit_fees_in_gbp as deposit_fees_in_gbp,
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waiver_payments_in_gbp as waiver_payments_in_gbp,
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checkin_cover_fees_in_gbp as checkin_cover_fees_in_gbp
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from int_monthly_agg_metrics_history_by_deal
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from int_monthly_aggregated_metrics_history_by_deal
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@ -11,9 +11,9 @@ with
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{% endif %}
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{% endfor %}
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),
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int_mtd_agg_metrics as (
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int_mtd_aggregated_metrics as (
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select m.*, d.dimension_display
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from {{ ref("int_mtd_agg_metrics") }} m
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from {{ ref("int_mtd_aggregated_metrics") }} m
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-- The following clause limits the display execution
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-- to only include those dimensions configured to
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-- appear for production purposes
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@ -38,7 +38,7 @@ select
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previous_year_value as previous_year_value,
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relative_increment as relative_increment,
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relative_increment_with_sign_format as relative_increment_with_sign_format
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from int_mtd_agg_metrics
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from int_mtd_aggregated_metrics
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/*
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The following where condition is applied to avoid displaying revenue metrics
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in the MTD for the current month and the previous month. The main reason is
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@ -307,7 +307,7 @@ models:
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tests:
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- not_null
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- name: mtd_agg_metrics
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- name: mtd_aggregated_metrics
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description: |
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This model aggregates the historic information of our business by providing
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different metrics computed at global and dimension level.
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@ -447,19 +447,19 @@ models:
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positive impact for Superhog, otherwise is equal to relative_increment.
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This value is specially created for formatting in PBI
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- name: monthly_agg_metrics_history_by_deal
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- name: monthly_aggregated_metrics_history_by_deal
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description: |
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This model aggregates the monthly historic information regarding the different metrics computed
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at deal level. The primary source of data is the `int_monthly_XXXXX_history_by_deal`
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model which contain the raw metrics data per source.
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This table is used to provide "By Deal" metrics in the Business Overview reporting.
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Unlike the mtd_agg_metrics, this model does not abstract each metric, since
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Unlike the mtd_aggregated_metrics, this model does not abstract each metric, since
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no comparison versus last year is performed. In short, it just gathers the information stored
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in the abovementioned models.
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To keep in mind: aggregating the information of this model will not necessarily result into
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the int_mtd_agg_metrics because 1) the mtd version contains more computing dates
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the int_mtd_aggregated_metrics because 1) the mtd version contains more computing dates
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than the by deal version, the latest being a subset of the first, and 2) the deal based model
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enforces that a booking/guest journey/listing/etc has a host with a deal assigned, which is
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not necessarily the case.
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