data-dwh-dbt-project/models/intermediate/cross/schema.yml

models:
  - name: int_daily_currency_exchange_rates
    description: >-
      This model holds a lot of data on currency exchange rates.  The time
      granularity is daily. Each record holds a currency pair for a specific
      day, source and version.

      Actual rates are sourced from xe.com data. The `guessed` and `forecast`
      versions are built by simply 'pushing'  the first/last exchange rate on
      record. Basically, wherever we dont' have data for a date, we pick the
      closest actual data point that comes from xe.com. Bear in mind this means
      that `forecast` version records will change on a daily basis as actual
      data moves forwards, meaning you shouldn't assume your money amounts
      converted in the future should always stay put.

      Note that, given the dimensionality, getting a simple time series for a
      currency pair will require a bit of filtering.

      Reverse rates are explicit. This means that, for any given day and any
      given currency pair, you will find two records with opposite from/to
      positions. So, for 2024-01-01, you will find both a EUR->USD record and a
      USD->EUR record with the opposite rate (1/rate).
    columns:
      - name: id_exchange_rate
        data_type: text
        description: A unique ID for the record, derived from concatenating the
          currencies, date, source and version. Currency order is relevant
          (EURUSD != USDEUR).
        data_tests:
          - not_null
          - unique
      - name: from_currency
        data_type: character
        description: The source currency, represented as an ISO 4217 code.
        data_tests:
          - not_null
      - name: to_currency
        data_type: character
        description: The target currency, represented as an ISO 4217 code.
        data_tests:
          - not_null
      - name: rate
        data_type: numeric
        description: >-
          The exchange rate, represented as the units of the target currency
          that one unit of source currency gets you. So, from_currency=USD to
          to_currency=PLN with rate=4.2 should be read as '1 US Dollar buys me
          4.2 Polish Zlotys'.

          For same currency pairs (EUR to EUR, USD to USD, etc). The rate will
          always be one.

          The rate can be smaller than one, but can't be negative.
        data_tests:
          - not_negative_or_zero
          - not_null
      - name: rate_date_utc
        data_type: date
        description: The date in which the rate record is relevant.
        data_tests:
          - not_null
      - name: source
        data_type: text
        description:
          Where is the data coming from. Records that are composed from
          making assumptions on real data will contain `_inferred`.
      - name: rate_version
        data_type: text
        description:
          The version of the rate. This can be one of `actual` (the rate is a
          reality fact), `forecast` (the rate sits in the future and is a guess
          in nature) or `guess` (the rate sits in the past and is a guess in
          nature). Note that one currency pair can have multiple rate versions
          on the same date.
        data_tests:
          - accepted_values:
              values:
                - guess
                - actual
                - forecast
          - not_null
      - name: updated_at_utc
        data_type: timestamp with time zone
        description:
          For external sources, this will be the point in time when the
          information was obtained from them. For stuff we make up here in the
          DWH, this will be the point in time when we made the assumption.
        data_tests:
          - not_null
  - name: int_simple_exchange_rates
    description: >-
      A simplified vision of exchange rates, derived from
      `int_daily_currency_exchange_rates`. Come here if you don't want to
      understand nuances and complexities and just want to convert rates.

      The time granularity is daily. Each record holds a currency pair for a
      specific day. You will only find one conversion rate per currency pair and
      date.
    data_tests:
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - from_currency
            - to_currency
            - rate_date_utc
    columns:
      - name: from_currency
        data_type: character
        description: The source currency, represented as an ISO 4217 code.
        data_tests:
          - not_null
      - name: to_currency
        data_type: character
        description: The source currency, represented as an ISO 4217 code.
        data_tests:
          - not_null
      - name: rate
        data_type: numeric
        description: The target currency, represented as an ISO 4217 code.
        data_tests:
          - not_null
      - name: rate_date_utc
        data_type: date
        description: The date in which the rate record is relevant.
        data_tests:
          - not_null
      - name: updated_at_utc
        data_type: timestamp with time zone
        description:
          For external sources, this will be the point in time when the
          information was obtained from them. For stuff we make up here in the
          DWH, this will be the point in time when we made the assumption.
        data_tests:
          - not_null

  - name: int_mtd_vs_previous_year_metrics
    description: |
      This model is used for global KPIs.

      It aggregates all the mtd models with the different metrics per source
      and computes any necessary weighted metric across different sources.
      Each metric has a date, dimension and dimension value that defines
      the primary key of this model.

      Finally, it displays any metric on the current date, the previous year
      date and it computes the relative increment by using the macro:
      - calculate_safe_relative_increment

    data_tests:
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - date
            - dimension
            - dimension_value

      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: revenue_retained_post_resolutions_in_gbp
          column_B: total_revenue_in_gbp + xero_waiver_paid_back_to_host_in_gbp + xero_host_resolution_amount_paid_in_gbp
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: total_revenue_in_gbp
          column_B: total_guest_payments_in_gbp + xero_apis_net_fees_in_gbp + xero_operator_net_fees_in_gbp

    columns:
      - name: date
        data_type: date
        description: The date for the month-to-date metrics.
        data_tests:
          - not_null

      - name: dimension
        data_type: string
        description: The dimension or granularity of the metrics.
        data_tests:
          - accepted_values:
              values:
                - global
                - by_number_of_listings
                - by_billing_country
                - by_business_scope
                - by_deal

      - name: dimension_value
        data_type: string
        description: The value or segment available for the selected dimension.
        data_tests:
          - not_null

  - name: int_mtd_aggregated_metrics
    description: |
      The `int_mtd_aggregated_metrics` model aggregates multiple metrics on a year, month, and day basis. 
      The primary source of data is the `int_mtd_vs_previous_year_metrics` model, which contain the combination
      of metrics data per source. This model just changes the display format to unpivot the information into
      a set of metric, value, previous_year_value and relative_increment at a given date. It uses Jinja 
      code to avoid code replication.

    data_tests:
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - date
            - metric
            - dimension
            - dimension_value

    columns:
      - name: year
        data_type: int
        description: year number of the given date.
        data_tests:
          - not_null

      - name: month
        data_type: int
        description: month number of the given date.
        data_tests:
          - not_null

      - name: day
        data_type: int
        description: day monthly number of the given date.
        data_tests:
          - not_null

      - name: is_end_of_month
        data_type: boolean
        description: True if it's end of month.
        data_tests:
          - not_null

      - name: is_end_of_month_or_yesterday
        data_type: boolean
        description: True if it's end of month or yesterday.
        data_tests:
          - not_null

      - name: is_current_month
        data_type: boolean
        description: |
          checks if the date is within the current executed month, 
          1 for yes, 0 for no.
        data_tests:
          - not_null

      - name: first_day_month
        data_type: date
        description: |
          first day of the month corresponding to the date field.
        data_tests:
          - not_null

      - name: date
        data_type: date
        description: |
          main date for the computation, that is used for filters.
        data_tests:
          - not_null

      - name: dimension
        data_type: string
        description: The dimension or granularity of the metrics.
        data_tests:
          - accepted_values:
              values:
                - global
                - by_number_of_listings
                - by_billing_country
                - by_business_scope
                - by_deal

      - name: dimension_value
        data_type: string
        description: The value or segment available for the selected dimension.
        data_tests:
          - not_null

      - name: previous_year_date
        data_type: date
        description: |
          corresponds to the date of the previous year, with respect to the field date. 
          It's only displayed for information purposes,
          should not be needed for reporting.

      - name: metric
        data_type: text
        description: name of the business metric.
        data_tests:
          - not_null

      - name: order_by
        data_type: integer
        description: |
          order for displaying purposes. Null values are accepted, but keep
          in mind that then there's no default controlled display order.

      - name: include_in_account_reporting
        data_type: boolean
        description: |
          Category to indicate if the metric should be included in the account
          reporting (by deal). This will limit the display for reporting purposes.
        data_tests:
          - not_null

      - name: display_exclusion
        data_type: string
        description: |
          Category to indicate if the metric requires a certain exclusion due 
          to relying on not timely information.
          This will limit the display for reporting purposes.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - NONE
                - INVOICING
                - ONGOING_MONTH

      - name: number_format
        data_type: text
        description: allows for grouping and formatting for displaying purposes.
        data_tests:
          - accepted_values:
              values:
                [
                  "integer",
                  "percentage",
                  "currency_gbp",
                  "converted_metric_currency_gbp",
                ]

      - name: value
        data_type: numeric
        description: |
          numeric value (integer or decimal) that corresponds to the MTD computation of the metric
          at a given date.

      - name: previous_year_value
        data_type: numeric
        description: |
          numeric value (integer or decimal) that corresponds to the MTD computation of the metric
          on the previous year at a given date.

      - name: relative_increment
        data_type: numeric
        description: |
          numeric value that corresponds to the relative increment between value and previous year value,
          following the computation: value / previous_year_value - 1.

      - name: relative_increment_with_sign_format
        data_type: numeric
        description: |
          relative_increment value multiplied by -1 in case this metric's growth doesn't have a
          positive impact for Superhog, otherwise is equal to relative_increment.
          This value is specially created for formatting in PBI

  - name: int_monthly_aggregated_metrics_history_by_deal
    deprecation_date: 2025-04-28 08:00:00
    description: |
      This model aggregates the monthly historic information regarding the different metrics computed
      at deal level. The primary sources of data are the `int_yyy__monthly_XXXXX_history_by_deal`
      models which contain the raw metrics data per source. 

      Unlike the int_mtd_aggregated_metrics, this model does not abstract each metric, since
      no comparison versus last year is performed. In short, it just gathers the information stored
      in the abovementioned models.

      To keep in mind: aggregating the information of this model will not necessarily result into
      the int_mtd_aggregated metrics because 1) the mtd version contains more computing dates
      than the by deal version, the latest being a subset of the first, and 2) the deal based model
      enforces that a booking/guest journey/listing/etc has a host with a deal assigned, which is
      not necessarily the case.

    data_tests:
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - date
            - id_deal

    columns:
      - name: date
        data_type: date
        description: The last day of the month or yesterday for historic metrics.
        data_tests:
          - not_null

      - name: id_deal
        data_type: character varying
        description: Id of the deal associated to the host.
        data_tests:
          - not_null

      - name: client_type
        data_type: string
        description: |
          Type of client. It can be either PLATFORM or API.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - PLATFORM
                - API

      - name: business_scope
        data_type: string
        description: |
          Business scope identifying the metric source.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - "Old Dash"
                - "New Dash"
                - "API"
                - "UNSET"

      - name: main_deal_name
        data_type: string
        description: |
          Main name for this ID deal.
        data_tests:
          - not_null

      - name: has_active_pms
        data_type: boolean
        description: |
          Does the deal have an active associated PMS.
        data_tests:
          - not_null

      - name: active_pms_list
        data_type: string
        description: |
          Name of the active PMS associated with the deal. It can have more than
          one PMS associated with it. It can be null if it doesn't have any PMS associated.

      - name: active_accommodations_per_deal_segmentation
        data_type: string
        description: |
          Segment value based on the number of listings booked in 12 months 
          for a given deal and date.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - "0"
                - "01-05"
                - "06-20"
                - "21-60"
                - "61+"
                - "UNSET"

      - name: main_billing_country_iso_3_per_deal
        data_type: string
        description: |
          ISO 3166-1 alpha-3 main country code in which the Deal is billed.
          In some cases it's null.

  - name: int_monthly_growth_score_by_deal
    deprecation_date: 2025-05-10 08:00:00
    description: |
      The main goal of this model is to provide a growth score by deal and month. 
      The idea behind it is that each deal will have some business performance 
      associated to it over the months, and that comparing how it is currently
      performing vs. historical data we can determine whether the tendency is to 
      grow or to decay. This is specially useful for AMs to focus their effort 
      towards the clients that have a negative tendency.

      The computation of the growth score is based on 3 main indicators:
      - Created bookings 
      - Listings booked in month 
      - Total revenue (in gbp)

      The main idea is, for each deal, to compare each of these metrics by 
      checking the latest monthly value vs. 1) the monthly value of the equivalent 
      month on the previous year and 2) the monthly value of the previous month
      - in other words, a year-on-year (YoY) and month-on-month (MoM) comparison.
      We do this comparison by doing a relative incremental.

      The growth score is computed then by averaging the outcome of the 6 scores. 
      Lastly, in order to provide a prioritisation sense, we have a weighted growth 
      score that results from the multiplication of the growth score per the revenue 
      weight a specific deal has provided in the previous 12 months.

      However, this is not strictly true for Revenue because 1) we have an invoicing 
      delay and 2) in some cases, monthly revenue per deal can be negative. In this 
      specific cases, the YoY comparison is shifted by one month, and an effective 
      revenue value for the revenue share is computed, that cannot be lower than 0. 

      In order to keep both a properly set up score and revenue consistency, both 
      a real revenue value and effective revenue value are present in this model, 
      while no MoM or YoY value is computed if negative revenue is found.  

      Lastly, this model provides informative date fields, deal attributes, absolute
      metric values and MoM & YoY relative incrementals to enrich reporting.

    data_tests:
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - date
            - id_deal

    columns:
      - name: date
        data_type: date
        description: |
          Date corresponding to the last day of the month. Given month 
          metrics are inclusive to this date. Together with id_deal, it 
          acts as the primary key of this model.
        data_tests:
          - not_null

      - name: id_deal
        data_type: string
        description: |
          Unique identifier of a Deal. Together with date, it acts as 
          the primary key of this model.
        data_tests:
          - not_null

      - name: client_type
        data_type: string
        description: |
          Type of client. It can be either PLATFORM or API.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - PLATFORM
                - API

      - name: main_deal_name
        data_type: string
        description: |
          Main name for a Deal, representing the client.
        data_tests:
          - not_null

      - name: has_active_pms
        data_type: boolean
        description: |
          Does the deal have an active associated PMS.
        data_tests:
          - not_null

      - name: active_pms_list
        data_type: string
        description: |
          Name of the active PMS associated with the deal. It can have more than
          one PMS associated with it. It can be null if it doesn't have any PMS associated.

      - name: main_billing_country_iso_3_per_deal
        data_type: string
        description: |
          Main billing country for this client. In some cases 
          it can be null.

      - name: deal_lifecycle_state
        data_type: string
        description: |
          Identifier of the lifecycle state of a given deal 
          in a given month.

      - name: deal_hubspot_stage
        data_type: string
        description: |
          Current hubspot stage for a given deal.

      - name: account_manager
        data_type: string
        description: |
          Current Account Manager in charge of a given deal, according
          to Hubspot.

      - name: live_date_utc
        data_type: date
        description: |
          Date in which the account has gone live, according to Hubspot.

      - name: cancellation_date_utc
        data_type: date
        description: |
          Date in which the account has been offboarded, according to 
          Hubspot.

      - name: given_month_first_day_month
        data_type: date
        description: |
          Informative field. It indicates the first day of the 
          month corresponding to date. 
          If date = 2024-09-30, this field will be 2024-09-01.
        data_tests:
          - not_null

      - name: previous_1_month_first_day_month
        data_type: date
        description: |
          Informative field. It indicates the first day of the 
          previous month with respect to date. 
          If date = 2024-09-30, this field will be 2024-08-01.
          It can be null if no previous history for that 
          deal is found.

      - name: previous_2_month_first_day_month
        data_type: date
        description: |
          Informative field. It indicates the first day of the 
          month 2 months before with respect to date. 
          If date = 2024-09-30, this field will be 2024-07-01.
          It can be null if no previous history for that 
          deal is found.

      - name: previous_12_month_first_day_month
        data_type: date
        description: |
          Informative field. It indicates the first day of the 
          month with respect to date, but on the previous year. 
          If date = 2024-09-30, this field will be 2023-09-01.
          It can be null if no previous history for that 
          deal is found.

      - name: previous_13_month_first_day_month
        data_type: date
        description: |
          Informative field. It indicates the first day of the 
          previous month with respect to date, but on the previous year. 
          If date = 2024-09-30, this field will be 2023-08-01.
          It can be null if no previous history for that 
          deal is found.

      - name: aggregated_revenue_from_first_day_month
        data_type: date
        description: |
          Informative field. It indicates the first day of the 
          month from the lower bound range in which the revenue 
          aggregation is computed.
          The aggregation uses the previous 12 months in which we 
          know the revenue, thus:
          If date = 2024-09-30, this field will be 2023-09-01.
          It can be null if no previous history for that 
          deal is found.

      - name: aggregated_revenue_to_first_day_month
        data_type: date
        description: |
          Informative field. It indicates the first day of the 
          month from the upper bound range in which the revenue 
          aggregation is computed.
          The aggregation uses the previous 12 months in which we 
          know the revenue, thus:
          If date = 2024-09-30, this field will be 2023-08-01.
          It can be null if no previous history for that 
          deal is found.

      - name: given_month_revenue_in_gbp
        data_type: decimal
        description: |
          Monthly value representing revenue in GBP 
          for a specific deal. This value corresponds to 
          the given month. This value can be negative,
          but not null.
        data_tests:
          - not_null

      - name: previous_1_month_revenue_in_gbp
        data_type: decimal
        description: |
          Monthly value representing revenue in GBP 
          for a specific deal. This value corresponds to 
          the previous month. 
          This value can be negative. 
          This value can be null, thus indicating that no 
          history is available.

      - name: previous_2_month_revenue_in_gbp
        data_type: decimal
        description: |
          Monthly value representing revenue in GBP 
          for a specific deal. This value corresponds to 
          the monthly amount generated 2 months ago
          This value can be negative. 
          This value can be null, thus indicating that no 
          history is available.

      - name: previous_12_month_revenue_in_gbp
        data_type: decimal
        description: |
          Monthly value representing revenue in GBP 
          for a specific deal. This value corresponds to 
          the monthly amount generated 12 months ago. 
          This value can be negative. 
          This value can be null, thus indicating that no 
          history is available.

      - name: previous_13_month_revenue_in_gbp
        data_type: decimal
        description: |
          Monthly value representing revenue in GBP 
          for a specific deal. This value corresponds to 
          the monthly amount generated 13 months ago. 
          This value can be negative. 
          This value can be null, thus indicating that no 
          history is available.

      - name: mom_revenue_growth
        data_type: decimal
        description: |
          Relative increment of the revenue generated in the 
          current month with respect to the one generated in 
          the previous month. 
          It can be null if any revenue used in the computation
          is null or it's negative.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: -1
              strictly: false

      - name: mom_1_month_shift_revenue_growth
        data_type: decimal
        description: |
          Relative increment of the revenue generated in the 
          previous month with respect to the one generated 2
          months ago. 
          It can be null if any revenue used in the computation
          is null or it's negative.
          This field is used for the growth score computation.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: -1
              strictly: false

      - name: yoy_revenue_growth
        data_type: decimal
        description: |
          Relative increment of the revenue generated in the 
          current month with respect to the one generated 12
          months ago. 
          It can be null if any revenue used in the computation
          is null or it's negative.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: -1
              strictly: false

      - name: yoy_1_month_shift_revenue_growth
        data_type: decimal
        description: |
          Relative increment of the revenue generated in the 
          previous month with respect to the one generated 13
          months ago. 
          It can be null if any revenue used in the computation
          is null or it's negative.
          This field is used for the growth score computation.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: -1
              strictly: false

      - name: given_month_created_bookings
        data_type: integer
        description: |
          Monthly value representing created bookings
          for a specific deal. This value corresponds to 
          the given month. This value cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: previous_1_month_created_bookings
        data_type: integer
        description: |
          Monthly value representing created bookings
          for a specific deal. This value corresponds to 
          the previous month. 
          This value can be null, thus indicating that no 
          history is available.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: previous_12_month_created_bookings
        data_type: integer
        description: |
          Monthly value representing created bookings 
          for a specific deal. This value corresponds to 
          monthly amount generated 12 months ago. 
          This value can be null, thus indicating that no 
          history is available.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: mom_created_bookings_growth
        data_type: decimal
        description: |
          Relative increment of the bookings created in the 
          current month with respect to the ones created in 
          the previous month. 
          It can be null if the bookings created in the 
          previous month are null.
          This field is used for the growth score computation.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: -1
              strictly: false

      - name: yoy_created_bookings_growth
        data_type: decimal
        description: |
          Relative increment of the bookings created in the 
          current month with respect to the ones created 12
          months ago. 
          It can be null if the bookings created 12 months 
          ago are null. 
          This field is used for the growth score computation.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: -1
              strictly: false

      - name: given_month_listings_booked_in_month
        data_type: integer
        description: |
          Monthly value representing the listings booked in month
          for a specific deal. This value corresponds to 
          the given month. This value cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: previous_1_month_listings_booked_in_month
        data_type: integer
        description: |
          Monthly value representing the listings booked in month
          for a specific deal. This value corresponds to 
          the previous month. 
          This value can be null, thus indicating that no 
          history is available.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: previous_12_month_listings_booked_in_month
        data_type: integer
        description: |
          Monthly value representing the listings booked in month
          for a specific deal. This value corresponds to 
          monthly amount generated 12 months ago. 
          This value can be null, thus indicating that no 
          history is available.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: mom_listings_booked_in_month_growth
        data_type: decimal
        description: |
          Relative increment of the the listings booked in month 
          in the current month with respect to the ones of 
          the previous month. 
          It can be null if the listings booked in month in the 
          previous month are null.
          This field is used for the growth score computation.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: -1
              strictly: false

      - name: yoy_listings_booked_in_month_growth
        data_type: decimal
        description: |
          Relative increment of the listings booked in month 
          in the current month with respect to the ones of 12
          months ago. 
          It can be null if the listings booked in month of 12 
          months ago are null. 
          This field is used for the growth score computation.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: -1
              strictly: false

      - name: deal_revenue_12_months_window
        data_type: decimal
        description: |
          Total aggregated revenue in GBP generated by a deal 
          in the months from the period ranging from the 
          aggregated_revenue_from_first_day_month to 
          aggregated_revenue_to_first_day_month. 
          It can be negative if the sum is negative. 
          It cannot be null.
        data_tests:
          - not_null

      - name: effective_deal_revenue_12_months_window
        data_type: decimal
        description: |
          Effective aggregated revenue in GBP generated by a deal 
          in the months from the period ranging from the 
          aggregated_revenue_from_first_day_month to 
          aggregated_revenue_to_first_day_month. 
          All negative monthly revenue values are settled as 0,
          thus this value should not be reported. 
          It is used for the deal contribution share with respect 
          to the global revenue. It cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: effective_global_revenue_12_months_window
        data_type: decimal
        description: |
          Effective aggregated revenue in GBP generated by all deals 
          in the months from the period ranging from the 
          aggregated_revenue_from_first_day_month to 
          aggregated_revenue_to_first_day_month. 
          All negative monthly revenue values are settled as 0,
          thus this value should not be reported. 
          It is used for the deal contribution share with respect 
          to the global revenue. It cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: deal_contribution_share_to_global_revenue
        data_type: decimal
        description: |
          Represents the size of the deal in terms of revenue. In
          other words, what's the percentage of the global revenue 
          that can be attributed to this deal. It cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: deal_contribution_rank_to_global_revenue
        data_type: integer
        description: |
          Represents the ordered list of deals by descending size 
          in terms of revenue. 
          If more than one deal have the same share, the order is 
          not under control.
          It cannot be null.
        data_tests:
          - not_null

      - name: deal_created_bookings_12_months_window
        data_type: integer
        description: |
          Total created bookings generated by a deal 
          in the months from the period ranging from the 
          aggregated_revenue_from_first_day_month to 
          aggregated_revenue_to_first_day_month. 
          It cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: global_created_bookings_12_months_window
        data_type: integer
        description: |
          Total created bookings generated by any deal 
          in the months from the period ranging from the 
          aggregated_revenue_from_first_day_month to 
          aggregated_revenue_to_first_day_month. 
          It is used for the deal contribution share with respect 
          to the global created bookings. It cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: deal_contribution_share_to_global_created_bookings
        data_type: decimal
        description: |
          Represents the size of the deal in terms of created bookings. 
          In other words, what's the percentage of the global created 
          bookings that can be attributed to this deal. 
          It cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: deal_contribution_rank_to_global_created_bookings
        data_type: integer
        description: |
          Represents the ordered list of deals by descending size 
          in terms of created bookings. 
          If more than one deal have the same share, the order is 
          not under control.
          It cannot be null.
        data_tests:
          - not_null

      - name: deal_avg_listings_booked_in_month_12_months_window
        data_type: decimal
        description: |
          Average listings booked in month by a deal 
          in the months from the period ranging from the 
          aggregated_revenue_from_first_day_month to 
          aggregated_revenue_to_first_day_month. 
          It cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: global_avg_listings_booked_in_month_12_months_window
        data_type: decimal
        description: |
          Sum of the average listings booked in month by 
          any deal in the months from the period ranging from the 
          aggregated_revenue_from_first_day_month to 
          aggregated_revenue_to_first_day_month. 
          It is used for the deal contribution share with respect 
          to the global average listings booked in month. 
          It cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: deal_contribution_share_to_global_avg_listings_booked_in_month
        data_type: decimal
        description: |
          Represents the size of the deal in terms of average listings 
          booked in month. 
          In other words, what's the percentage of the global average listings
          booked in month that can be attributed to this deal. 
          It cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: deal_contribution_rank_to_global_avg_listings_booked_in_month
        data_type: decimal
        description: |
          Represents the ordered list of deals by descending size 
          in terms of average listings booked in month. 
          If more than one deal have the same share, the order is 
          not under control.
          It cannot be null.
        data_tests:
          - not_null

      - name: avg_mom_growth_score
        data_type: decimal
        description: |
          Represents the average score of MoM growth of created 
          bookings, MoM growth of listings booked in month and 
          MoM shifted by one month of revenue.
          It indicates the tendency of growth of the deal without 
          taking into account its revenue size. It cannot be null.
        data_tests:
          - not_null

      - name: avg_yoy_growth_score
        data_type: decimal
        description: |
          Represents the average score of YoY growth of created 
          bookings, YoY growth of listings booked in month and 
          YoY shifted by one month of revenue.
          It indicates the tendency of growth of the deal without 
          taking into account its revenue size. It cannot be null.
        data_tests:
          - not_null

      - name: avg_growth_score
        data_type: decimal
        description: |
          Represents the average score of YoY and MoM growth of created 
          bookings, YoY and MoM growth of listings booked in month and 
          YoY and MoM shifted by one month of revenue.
          It indicates the tendency of growth of the deal without 
          taking into account its revenue size. It cannot be null.
        data_tests:
          - not_null

      - name: weighted_avg_growth_score
        data_type: decimal
        description: |
          It's the weighted version of avg_growth_score that 
          takes into account the client size by using the revenue 
          contribution share of that deal to the global amount.
          It's the main indicator towards measuring both growth 
          (if positive) or decay (if negative) while weighting 
          the financial impact this deal tendency can have.
        data_tests:
          - not_null

      - name: categorisation_weighted_avg_growth_score
        data_type: string
        description: |
          Discrete categorisation of weighted_avg_growth_score.
          It helps easily identifying which accounts are top losers, 
          losers, flat, winners and top winners. 
          Currently the categorisation is based on the score itself 
          rather than selecting a top up/down.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - MAJOR DECLINE
                - DECLINE
                - FLAT
                - GAIN
                - MAJOR GAIN
                - UNSET

  - name: int_edeposit_and_athena_verifications
    description:
      "This table holds records on verifications for Guesty and Edeposit bookings.
      It contains details on validations checked on the guests, guest information
      and some booking details like checkin-checkout date or the status of the verification.
      The id values found here are completely unrelated to the ones found in Core DWH.

      Note that id_verifications and booking_id should normally be 1 to 1.
      Though there are exception, the API will accept a duplicate booking and the users
      will be charged for it. A duplicate would return a unique id_verification."
    columns:
      - name: id_verification
        data_type: text
        description: "unique Superhog generated id for this verification"
        data_tests:
          - unique
          - not_null

      - name: id_booking
        data_type: text
        description: "unique Superhog generated id for a booking.
          note that this could be duplicated and both will be charged,
          it's up to the user to no generate duplicate verifications"

      - name: id_user_partner
        data_type: text
        description: "unique Superhog generated id for partner"
        data_tests:
          - not_null

      - name: id_accommodation
        data_type: text
        description: "unique Superhog generated id for a listing"

      - name: version
        data_type: text
        description: "value to identify if it is Guesty (V1) or E-deposit (V2)"
        data_tests:
          - accepted_values:
              values:
                - V1
                - V2

      - name: verification_source
        data_type: text
        description: "source of the verification for the booking"
        data_tests:
          - accepted_values:
              values:
                - Guesty
                - Edeposit

      - name: verification_status
        data_type: text
        description: "status of the verification"

      - name: nightly_fee_local
        data_type: double precision
        description: "fee in local currency charged per night"

      - name: number_nights
        data_type: integer
        description: "number of nights for the booking"

      - name: total_fee_local
        data_type: double precision
        description: "total fee in local currency for the booking"

      - name: email_flag
        data_type: text
        description: "screening result for email"

      - name: phone_flag
        data_type: text
        description: "screening result for phone"

      - name: watch_list
        data_type: text
        description: "screening result of the guest"

      - name: channel
        data_type: text
        description: ""

      - name: checkin_at_utc
        data_type: timestamp without time zone
        description: "Timestamp of checkin for the booking"

      - name: checkin_date_utc
        data_type: date
        description: "Date of checkin for the booking"

      - name: checkout_at_utc
        data_type: timestamp without time zone
        description: "Timestamp of checkout for the booking"

      - name: checkout_date_utc
        data_type: date
        description: "Date of checkout for the booking"

      - name: is_cancelled
        data_type: boolean
        description: ""

      - name: cancelled_at_utc
        data_type: timestamp without time zone
        description: "Timestamp of cancellation of the booking"

      - name: cancelled_date_utc
        data_type: date
        description: "Date of cancellation for the booking"

      - name: user_email
        data_type: text
        description: ""

      - name: guest_email
        data_type: text
        description: ""

      - name: guest_last_name
        data_type: text
        description: ""

      - name: guest_first_name
        data_type: text
        description: ""

      - name: guest_telephone
        data_type: text
        description: ""

      - name: company_name
        data_type: text
        description: ""

      - name: property_manager_name
        data_type: text
        description: ""

      - name: property_manager_email
        data_type: text
        description: ""

      - name: listing_name
        data_type: text
        description: ""

      - name: listing_address
        data_type: text
        description: ""

      - name: listing_town
        data_type: text
        description: ""

      - name: listing_country
        data_type: text
        description: ""

      - name: listing_postcode
        data_type: text
        description: ""

      - name: pets_allowed
        data_type: boolean
        description: ""

      - name: level_of_protection_amount
        data_type: integer
        description: ""

      - name: level_of_protection_currency
        data_type: text
        description: ""

      - name: status_updated_at_utc
        data_type: timestamp without time zone
        description: "Timestamp when status was last updated"

      - name: status_updated_date_utc
        data_type: date
        description: "Date of last status update of the verification"

      - name: updated_at_utc
        data_type: timestamp without time zone
        description: "Timestamp of last updated of the verification"

      - name: updated_date_utc
        data_type: date
        description: "Date of last update of the verification"

      - name: athena_creation_at_utc
        data_type: timestamp without time zone
        description:
          "Athena timestamp referring to when the booking was created.
          It's provided by Guesty, but is not mandatory.
          In case of doubt use created_at_utc or created_date_utc fields"

      - name: athena_creation_date_utc
        data_type: date
        description: "Athena date referring to when the booking was created.
          It's provided by Guesty, but is not mandatory.
          In case of doubt use created_at_utc or created_date_utc fields"

      - name: created_at_utc
        data_type: timestamp without time zone
        description: "Timestamp of creation of the verification in the system"

      - name: created_date_utc
        data_type: date
        description: "Date of creation of the verification in the system"

  - name: int_monthly_aggregated_metrics_history_by_deal_by_time_window
    description: |
      This model aggregates monthly historic metrics for deals over different time windows.
      It provides insights into bookings, listings, revenue, retained revenue and 
      additional metrics.
      The data is segmented by deal and time window for detailed analysis.

    data_tests:
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - date
            - id_deal
            - time_window

      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: total_revenue_in_gbp
          column_B: invoiced_revenue_in_gbp + guest_payments_in_gbp

      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: revenue_retained_in_gbp
          column_B: total_revenue_in_gbp + waiver_paid_back_to_host_in_gbp

      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: revenue_retained_post_resolutions_in_gbp
          column_B: revenue_retained_in_gbp + host_resolution_amount_paid_in_gbp

      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: guest_revenue_retained_in_gbp
          column_B: guest_payments_in_gbp + waiver_paid_back_to_host_in_gbp

    columns:
      - name: date
        data_type: date
        description: |
          The last day of the month or yesterday for historic metrics.
          It's the same date as for KPIs related models.
        data_tests:
          - not_null

      - name: id_deal
        data_type: character varying
        description: Id of the deal associated to the host.
        data_tests:
          - not_null

      - name: time_window
        data_type: character varying
        description: |
          Identifier of the time window used for the aggregation of the metrics.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - All History
                - Previous 12 months
                - Previous 6 months
                - Previous 3 months
                - Previous month

      - name: client_type
        data_type: string
        description: |
          Type of client. It can be either PLATFORM or API.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - PLATFORM
                - API

      - name: business_scope
        data_type: string
        description: |
          Business scope identifying the metric source.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - "Old Dash"
                - "New Dash"
                - "API"
                - "UNSET"

      - name: metric_from_date
        data_type: date
        description: |
          The first day of the month corresponding to the lower bound
          range in which the metric is computed. It can be null if 
          there's no previous history for that deal. It can vary from 
          deal to deal depending on the number of months the deal has 
          been active.

      - name: metric_to_date
        data_type: date
        description: |
          The first day of the month corresponding to the upper bound
          range in which the metric is computed. It can be null if 
          there's no previous history for that deal.

      - name: main_deal_name
        data_type: string
        description: |
          Main name for this ID deal.
        data_tests:
          - not_null

      - name: has_active_pms
        data_type: boolean
        description: |
          Does the deal have an active associated PMS.
        data_tests:
          - not_null

      - name: active_pms_list
        data_type: string
        description: |
          Name of the active PMS associated with the deal. It can have more than
          one PMS associated with it. It can be null if it doesn't have any PMS associated.

      - name: active_accommodations_per_deal_segmentation
        data_type: string
        description: |
          Segment value based on the number of listings booked in 12 months 
          for a given deal and date.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - "0"
                - "01-05"
                - "06-20"
                - "21-60"
                - "61+"
                - "UNSET"

      - name: main_billing_country_iso_3_per_deal
        data_type: string
        description: |
          ISO 3166-1 alpha-3 main country code in which the Deal is billed.
          In some cases it's null.

      - name: deal_lifecycle_state
        data_type: string
        description: |
          Lifecycle state of the deal.

      - name: deal_hubspot_stage
        data_type: string
        description: |
          Hubspot stage of the deal.
          In some cases it's null.

      - name: account_manager
        data_type: string
        description: |
          Account manager of the deal.
          In some cases it's null.

      - name: live_date_utc
        data_type: date
        description: |
          Date when the deal went live according to 
          Hubspot. In some cases it's null.

      - name: cancellation_date_utc
        data_type: date
        description: |
          Date when the deal was cancelled according to 
          Hubspot. It can be null if the deal has never 
          churned.

      - name: months_between_live_and_churn
        data_type: integer
        description: |
          Number of months between the live date and the 
          churn date.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: last_contacted_date_utc
        data_type: date
        description: |
          Date when the deal was last contacted according to 
          Hubspot.

      - name: days_between_last_contacted_and_churn
        data_type: integer
        description: |
          Number of days between the last contacted date 
          and the churn date.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: amount_times_contacted
        data_type: integer
        description: |
          Number of times the deal was contacted according 
          to Hubspot.

      - name: cancellation_category
        data_type: text
        description: Categorization as to why they cancelled the account

      - name: cancellation_details
        data_type: text
        description: Free text with additional comments on why they cancelled the account

      - name: is_churning
        data_type: boolean
        description: |
          Flag to identify if the deal is churning or not.

      - name: churn_reason
        data_type: string
        description: |
          Reason why the deal is churning.
        data_tests:
          - accepted_values:
              values:
                - "INACTIVITY"
                - "ACCOUNT CANCELLATION"

      - name: created_bookings
        data_type: integer
        description: |
          Total amount of bookings created by the deal 
          in the time window. It can be null if no bookings 
          were created.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: listings_booked_in_month
        data_type: decimal
        description: |
          Average amount of listings booked in month by the deal 
          in the time window. It can be null if no listings 
          were booked.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: total_revenue_in_gbp
        data_type: decimal
        description: |
          Total revenue in GBP generated by the deal in the 
          time window. It can be null if no revenue was generated.
          It can be negative.

      - name: revenue_retained_in_gbp
        data_type: decimal
        description: |
          Total revenue in GBP retained by the deal in the 
          time window, post host takeaway waivers. 
          It can be null if no revenue was retained.
          It can be negative.

      - name: waiver_paid_back_to_host_in_gbp
        data_type: decimal
        description: |
          Total amount of waivers paid back to the host in GBP 
          in the time window. It can be null if no waivers were 
          paid back. It's displayed as a negative value.

      - name: invoiced_revenue_in_gbp
        data_type: decimal
        description: |
          Total amount of revenue in GBP invoiced to the host 
          in the time window. It considers both Operator revenue as
          well as APIs revenue. It can be null if no revenue was 
          invoiced to the host. It can be negative.

      - name: guest_payments_in_gbp
        data_type: decimal
        description: |
          Total amount of payments in GBP made by the guest 
          in the time window. It can be null if no payments 
          were made by the guest. It can be negative.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: guest_revenue_retained_in_gbp
        data_type: decimal
        description: |
          Total amount of revenue in GBP retained by the deal 
          from the guest in the time window, post host takeaway waivers.
          It can be null if no revenue was retained from the guest. 
          It can be negative.

      - name: host_resolution_payment_count
        data_type: integer
        description: |
          Total amount of resolution payments made to the host 
          in the time window. It can be null if no resolution 
          payments were made by the host.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: host_resolution_amount_paid_in_gbp
        data_type: decimal
        description: |
          Total amount of resolution payments made to the host 
          in GBP in the time window. It can be null if no resolution 
          payments were made by the host. It can be negative.
          It's displayed as a negative value. In some extreme 
          cases, it can be higher than 0.

      - name: revenue_retained_post_resolutions_in_gbp
        data_type: decimal
        description: |
          Total amount of revenue in GBP retained by the deal 
          post waiver payouts and resolution payouts in the time window. 
          It can be null if no revenue was retained post resolution payments.
          It can be negative, thus indicating that we are losing money.

  - name: int_deals_consolidation
    description: |
      "This table contains all deal ids from different sources used in Superhog. 
      It contains the source (Hubspot, Xero or Core), the id_deal and the name"
    columns:
      - name: id_deal
        data_type: character varying
        description: "Unique ID for this deal."
        data_tests:
          - unique
          - not_null

      - name: core_company_name
        data_type: character varying
        description: "Company name of the deal as shown in Core."

      - name: core_company_name_count
        data_type: integer
        description: "Count of distinct names the deal has in Core.
          It might be the case that a deal has ony NULL value for a name,
          so the count will be 0"
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: hubspot_deal_name
        data_type: character varying
        description: "Name of the deal as shown in Hubspot."

      - name: hubspot_deal_name_count
        data_type: integer
        description: "Count of distinct names the deal has in Hubspot.
          It might be the case that a deal has ony NULL value for a name,
          so the count will be 0"
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: xero_contact_name
        data_type: character varying
        description: "Contact name of the deal as shown in Xero."

      - name: xero_contact_name_count
        data_type: integer
        description: "Count of distinct names the deal has in Xero.
          It might be the case that a deal has ony NULL value for a name,
          so the count will be 0"
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: is_deal_in_core
        data_type: boolean
        description: "Flag to indicate if the deal is in Core."

      - name: is_deal_in_hubspot
        data_type: boolean
        description: "Flag to indicate if the deal is in Hubspot."

      - name: is_deal_in_xero
        data_type: boolean
        description: "Flag to indicate if the deal is in Xero."

  - name: int_ytd_mtd_main_metrics_overview
    description: |
      This model provides a high-level overview of the main metrics for the month-to-date 
      and financial year-to-date periods.

    data_tests:
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - date
            - dimension
            - dimension_value

      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: current_month_mtd_revenue_retained_post_resolutions_in_gbp
          column_B: current_month_mtd_total_revenue_in_gbp + current_month_mtd_xero_waiver_paid_back_to_host_in_gbp + current_month_mtd_xero_host_resolution_amount_paid_in_gbp
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: current_ytd_revenue_retained_post_resolutions_in_gbp
          column_B: current_ytd_total_revenue_in_gbp + current_ytd_xero_waiver_paid_back_to_host_in_gbp + current_ytd_xero_host_resolution_amount_paid_in_gbp
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: current_month_mtd_total_revenue_in_gbp
          column_B: current_month_mtd_total_guest_payments_in_gbp + current_month_mtd_xero_apis_net_fees_in_gbp + current_month_mtd_xero_operator_net_fees_in_gbp
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: current_ytd_total_revenue_in_gbp
          column_B: current_ytd_total_guest_payments_in_gbp + current_ytd_xero_apis_net_fees_in_gbp + current_ytd_xero_operator_net_fees_in_gbp

    columns:
      - name: date
        data_type: date
        description: |
          The date for the month-to-date and year-to-date metrics.
        data_tests:
          - not_null

      - name: dimension
        data_type: string
        description: |
          The dimension or granularity of the metrics.
        data_tests:
          - accepted_values:
              values:
                - global

      - name: dimension_value
        data_type: string
        description: |
          The value or segment available for the selected dimension.
        data_tests:
          - not_null

      - name: calendar_year
        data_type: integer
        description: |
          The calendar year associated with the data.

      - name: financial_year
        data_type: integer
        description: |
          The financial year associated with the data.

      - name: previous_year_date
        data_type: date
        description: |
          The equivalent date in the previous year.

      - name: current_month_mtd_billable_bookings
        data_type: integer
        description: |
          Total billable bookings for the current month MTD.

      - name: current_month_mtd_churning_deals
        data_type: integer
        description: |
          Number of churning deals for the current month MTD.

      - name: current_month_mtd_live_deals
        data_type: integer
        description: |
          Number of live deals for the current month MTD.

      - name: current_month_mtd_new_deals
        data_type: integer
        description: |
          Number of new deals for the current month MTD.

      - name: current_month_mtd_operator_revenue_per_billable_booking
        data_type: numeric
        description: |
          Operator revenue per billable booking for the 
          current month MTD.

      - name: current_month_mtd_resolutions_payout_rate
        data_type: numeric
        description: |
          Resolutions payout rate for the current month MTD.

      - name: current_month_mtd_revenue_retained_post_resolutions_in_gbp
        data_type: numeric
        description: |
          Revenue retained after resolutions for the current 
          month MTD in GBP.

      - name: current_month_mtd_total_guest_payments_in_gbp
        data_type: numeric
        description: |
          Total guest payments for the current month MTD in GBP.

      - name: current_month_mtd_total_revenue_in_gbp
        data_type: numeric
        description: |
          Total revenue for the current month MTD in GBP.

      - name: current_month_mtd_waiver_payments_in_gbp
        data_type: numeric
        description: |
          Total waiver payments made in GBP for the current month MTD.

      - name: current_month_mtd_waiver_payout_rate
        data_type: numeric
        description: |
          Waiver payout rate for the current month MTD.

      - name: current_month_mtd_waiver_revenue_per_billable_booking
        data_type: numeric
        description: |
          Waiver revenue per billable booking for the current month MTD.

      - name: current_month_mtd_xero_apis_net_fees_in_gbp
        data_type: numeric
        description: |
          Net fees for APIs processed through Xero for 
          the current month MTD in GBP.

      - name: current_month_mtd_xero_host_resolution_amount_paid_in_gbp
        data_type: numeric
        description: |
          Amount paid to hosts for resolutions processed 
          through Xero for the current month MTD in GBP.

      - name: current_month_mtd_xero_operator_net_fees_in_gbp
        data_type: numeric
        description: |
          Net fees for operators processed through Xero 
          for the current month MTD in GBP.

      - name: current_month_mtd_xero_waiver_paid_back_to_host_in_gbp
        data_type: numeric
        description: |
          Waiver amounts paid back to hosts via Xero 
          for the current month MTD in GBP.

      - name: current_month_mtd_total_revenue_churn_12m
        data_type: numeric
        description: |
          Total revenue in the last 12 months that was generated by 
          deals that have churned in the current month MTD.

      - name: current_month_mtd_total_revenue_global_12m
        data_type: numeric
        description: |
          Total revenue in the last 12 months that was generated by any
          deal, indistinctly of it being active, churn, etc. for the 
          current month MTD. This is only used to compute the churn rate.

      - name: current_month_mtd_total_revenue_churn_rate
        data_type: numeric
        description: |
          Total revenue churn rate for the current month MTD.

      - name: current_month_mtd_booking_net_fees_in_gbp
        data_type: numeric
        description: |
          Total Booking net fees in gbp for the current month MTD.

      - name: current_month_mtd_booking_net_fees_per_billable_booking
        data_type: numeric
        description: |
          Total Booking net fees per billable booking for the current month MTD.

      - name: previous_year_mtd_billable_bookings
        data_type: integer
        description: |
          Total billable bookings for the previous year (12 months ago) MTD.

      - name: previous_year_mtd_churning_deals
        data_type: integer
        description: |
          Number of churning deals for the previous year (12 months ago) MTD.

      - name: previous_year_mtd_live_deals
        data_type: integer
        description: |
          Number of live deals for the previous year (12 months ago) MTD.

      - name: previous_year_mtd_new_deals
        data_type: integer
        description: |
          Number of new deals for the previous year (12 months ago) MTD.

      - name: previous_year_mtd_operator_revenue_per_billable_booking
        data_type: numeric
        description: |
          Operator revenue per billable booking for the 
          previous year (12 months ago) MTD.

      - name: previous_year_mtd_resolutions_payout_rate
        data_type: numeric
        description: |
          Resolutions payout rate for the previous year 
          (12 months ago) MTD.

      - name: previous_year_mtd_revenue_retained_post_resolutions_in_gbp
        data_type: numeric
        description: |
          Revenue retained after resolutions for the previous year 
          (12 months ago) MTD in GBP.

      - name: previous_year_mtd_total_guest_payments_in_gbp
        data_type: numeric
        description: |
          Total guest payments for the previous year (12 months ago) 
          MTD in GBP.

      - name: previous_year_mtd_total_revenue_in_gbp
        data_type: numeric
        description: |
          Total revenue for the previous year (12 months ago) MTD in GBP.

      - name: previous_year_mtd_waiver_payments_in_gbp
        data_type: numeric
        description: |
          Total waiver payments made in GBP for the previous 
          year (12 months ago) MTD.

      - name: previous_year_mtd_waiver_payout_rate
        data_type: numeric
        description: |
          Waiver payout rate for the previous year (12 months ago) MTD.

      - name: previous_year_mtd_waiver_revenue_per_billable_booking
        data_type: numeric
        description: |
          Waiver revenue per billable booking for the 
          previous year (12 months ago) MTD.

      - name: previous_year_mtd_xero_apis_net_fees_in_gbp
        data_type: numeric
        description: |
          Net fees for APIs processed through Xero for the 
          previous year (12 months ago) MTD in GBP.

      - name: previous_year_mtd_xero_host_resolution_amount_paid_in_gbp
        data_type: numeric
        description: |
          Amount paid to hosts for resolutions processed through 
          Xero for the previous year (12 months ago) MTD in GBP.

      - name: previous_year_mtd_xero_operator_net_fees_in_gbp
        data_type: numeric
        description: |
          Net fees for operators processed through Xero for the 
          previous year (12 months ago) MTD in GBP.

      - name: previous_year_mtd_xero_waiver_paid_back_to_host_in_gbp
        data_type: numeric
        description: |
          Waiver amounts paid back to hosts via Xero for the 
          previous year (12 months ago) MTD in GBP.

      - name: previous_year_mtd_total_revenue_churn_12m
        data_type: numeric
        description: |
          Total revenue in the last 12 months that was generated by 
          deals that have churned in the previous year 
          (12 months ago) MTD.

      - name: previous_year_mtd_total_revenue_global_12m
        data_type: numeric
        description: |
          Total revenue generated globally in the last 12 months 
          for the previous year (12 months ago) MTD. 
          This is only used to compute the churn rate.

      - name: previous_year_mtd_total_revenue_churn_rate
        data_type: numeric
        description: |
          Total revenue churn rate for the previous year 
          (12 months ago) MTD.

      - name: previous_year_mtd_booking_net_fees_in_gbp
        data_type: numeric
        description: |
          Total Booking net fees in gbp for the previous 
          year (12 months ago) MTD.

      - name: previous_year_mtd_booking_net_fees_per_billable_booking
        data_type: numeric
        description: |
          Total Booking net fees per billable booking for the 
          previous year (12 months ago) MTD.

      - name: current_ytd_billable_bookings
        data_type: integer
        description: |
          Total billable bookings for the current financial year YTD.

      - name: current_ytd_churning_deals
        data_type: integer
        description: |
          Number of churning deals for the current financial year YTD.

      - name: current_ytd_live_deals
        data_type: integer
        description: |
          Number of live deals for the current financial year YTD.

      - name: current_ytd_new_deals
        data_type: integer
        description: |
          Number of new deals for the current financial year YTD.

      - name: current_ytd_operator_revenue_per_billable_booking
        data_type: numeric
        description: |
          Operator revenue per billable booking for the current 
          financial year YTD.

      - name: current_ytd_resolutions_payout_rate
        data_type: numeric
        description: |
          Resolutions payout rate for the current financial year YTD.

      - name: current_ytd_revenue_retained_post_resolutions_in_gbp
        data_type: numeric
        description: |
          Revenue retained after resolutions for the current 
          financial year YTD in GBP.

      - name: current_ytd_total_guest_payments_in_gbp
        data_type: numeric
        description: |
          Total guest payments for the current financial year 
          YTD in GBP.

      - name: current_ytd_total_revenue_in_gbp
        data_type: numeric
        description: |
          Total revenue for the current financial year YTD in GBP.

      - name: current_ytd_waiver_payments_in_gbp
        data_type: numeric
        description: |
          Total waiver payments made in GBP for the current 
          financial year YTD.

      - name: current_ytd_waiver_payout_rate
        data_type: numeric
        description: |
          Waiver payout rate for the current financial year YTD.

      - name: current_ytd_waiver_revenue_per_billable_booking
        data_type: numeric
        description: |
          Waiver revenue per billable booking for the current 
          financial year YTD.

      - name: current_ytd_xero_apis_net_fees_in_gbp
        data_type: numeric
        description: |
          Net fees for APIs processed through Xero for 
          the current financial year YTD in GBP.

      - name: current_ytd_xero_host_resolution_amount_paid_in_gbp
        data_type: numeric
        description: |
          Amount paid to hosts for resolutions processed through 
          Xero for the current financial year YTD in GBP.

      - name: current_ytd_xero_operator_net_fees_in_gbp
        data_type: numeric
        description: |
          Net fees for operators processed through Xero 
          for the current financial year YTD in GBP.

      - name: current_ytd_xero_waiver_paid_back_to_host_in_gbp
        data_type: numeric
        description: |
          Waiver amounts paid back to hosts via Xero 
          for the current financial year YTD in GBP.

      - name: current_ytd_total_revenue_churn_12m
        data_type: numeric
        description: |
          Total revenue in the last 12 months that was generated by 
          deals that have churned in the period of the current 
          financial year YTD.

      - name: current_ytd_total_revenue_global_12m
        data_type: numeric
        description: |
          Total revenue generated globally in the last 12 months 
          for the current financial year YTD.
          This is only used to compute the churn rate.

      - name: current_ytd_total_revenue_churn_rate
        data_type: numeric
        description: |
          Total revenue churn rate for the current financial year YTD.

      - name: current_ytd_booking_net_fees_in_gbp
        data_type: numeric
        description: |
          Total booking net fees in gbp for the current financial year YTD.

      - name: current_ytd_booking_net_fees_per_billable_booking
        data_type: numeric
        description: |
          Total booking net fees per billable booking for the 
          current financial year YTD.

      - name: previous_ytd_billable_bookings
        data_type: integer
        description: |
          Total billable bookings for the previous financial year YTD.

      - name: previous_ytd_churning_deals
        data_type: integer
        description: |
          Number of churning deals for the previous financial 
          year YTD.

      - name: previous_ytd_live_deals
        data_type: integer
        description: |
          Number of live deals for the previous financial year YTD.

      - name: previous_ytd_new_deals
        data_type: integer
        description: |
          Number of new deals for the previous financial year YTD.

      - name: previous_ytd_operator_revenue_per_billable_booking
        data_type: numeric
        description: |
          Operator revenue per billable booking for the previous 
          financial year YTD.

      - name: previous_ytd_resolutions_payout_rate
        data_type: numeric
        description: |
          Resolutions payout rate for the previous financial year YTD.

      - name: previous_ytd_revenue_retained_post_resolutions_in_gbp
        data_type: numeric
        description: |
          Revenue retained after resolutions for the previous 
          financial year YTD in GBP.

      - name: previous_ytd_total_guest_payments_in_gbp
        data_type: numeric
        description: |
          Total guest payments for the previous financial year YTD in GBP.

      - name: previous_ytd_total_revenue_in_gbp
        data_type: numeric
        description: |
          Total revenue for the previous financial year YTD in GBP.

      - name: previous_ytd_waiver_payments_in_gbp
        data_type: numeric
        description: |
          Total waiver payments made in GBP for the previous
          financial year YTD.

      - name: previous_ytd_waiver_payout_rate
        data_type: numeric
        description: |
          Waiver payout rate for the previous financial year YTD.

      - name: previous_ytd_waiver_revenue_per_billable_booking
        data_type: numeric
        description: |
          Waiver revenue per billable booking for 
          the previous financial year YTD.

      - name: previous_ytd_xero_apis_net_fees_in_gbp
        data_type: numeric
        description: |
          Net fees for APIs processed through Xero 
          for the previous financial year YTD in GBP.

      - name: previous_ytd_xero_host_resolution_amount_paid_in_gbp
        data_type: numeric
        description: |
          Amount paid to hosts for resolutions processed 
          through Xero for the previous financial year YTD in GBP.

      - name: previous_ytd_xero_operator_net_fees_in_gbp
        data_type: numeric
        description: |
          Net fees for operators processed through Xero 
          for the previous financial year YTD in GBP.

      - name: previous_ytd_xero_waiver_paid_back_to_host_in_gbp
        data_type: numeric
        description: |
          Waiver amounts paid back to hosts via Xero 
          for the previous financial year YTD in GBP.

      - name: previous_ytd_total_revenue_churn_12m
        data_type: numeric
        description: |
          Total revenue in the last 12 months that was generated by 
          deals that have churned in the period of the previous 
          financial year YTD.

      - name: previous_ytd_total_revenue_global_12m
        data_type: numeric
        description: |
          Total revenue generated globally in the last 12 months 
          for the previous financial year YTD.
          This is only used to compute the churn rate.

      - name: previous_ytd_total_revenue_churn_rate
        data_type: numeric
        description: |
          Total revenue churn rate for the previous 
          financial year YTD.

      - name: previous_ytd_booking_net_fees_in_gbp
        data_type: numeric
        description: |
          Total booking net fees in gbp for the previous financial year YTD.

      - name: previous_ytd_booking_net_fees_per_billable_booking
        data_type: numeric
        description: |
          Total booking net fees per billable booking for the 
          previous financial year YTD.

      - name: previous_month_eom_billable_bookings
        data_type: integer
        description: |
          Total billable bookings for the previous month, 
          at the end of the month.

      - name: previous_month_eom_churning_deals
        data_type: integer
        description: |
          Number of churning deals for the previous month, 
          at the end of the month.

      - name: previous_month_eom_live_deals
        data_type: integer
        description: |
          Number of live deals for the previous month, 
          at the end of the month.

      - name: previous_month_eom_new_deals
        data_type: integer
        description: |
          Number of new deals for the previous month, 
          at the end of the month.

      - name: previous_month_eom_operator_revenue_per_billable_booking
        data_type: numeric
        description: |
          Operator revenue per billable booking for 
          the previous month, at the end of the month.

      - name: previous_month_eom_resolutions_payout_rate
        data_type: numeric
        description: |
          Resolutions payout rate for the previous month, 
          at the end of the month.

      - name: previous_month_eom_revenue_retained_post_resolutions_in_gbp
        data_type: numeric
        description: |
          Revenue retained after resolutions for the 
          previous month, at the end of the month in GBP.

      - name: previous_month_eom_total_guest_payments_in_gbp
        data_type: numeric
        description: |
          Total guest payments for the previous month, 
          at the end of the month in GBP.

      - name: previous_month_eom_total_revenue_in_gbp
        data_type: numeric
        description: |
          Total revenue for the previous month, at the 
          end of the month in GBP.

      - name: previous_month_eom_waiver_payments_in_gbp
        data_type: numeric
        description: |
          Total waiver payments made in GBP for the previous 
          month, at the end of the month.

      - name: previous_month_eom_waiver_payout_rate
        data_type: numeric
        description: |
          Waiver payout rate for the previous month, 
          at the end of the month.

      - name: previous_month_eom_waiver_revenue_per_billable_booking
        data_type: numeric
        description: |
          Waiver revenue per billable booking for the previous month, 
          at the end of the month.

      - name: previous_month_eom_xero_apis_net_fees_in_gbp
        data_type: numeric
        description: |
          Net fees for APIs processed through Xero for the previous month, 
          at the end of the month in GBP.

      - name: previous_month_eom_xero_host_resolution_amount_paid_in_gbp
        data_type: numeric
        description: |
          Amount paid to hosts for resolutions processed through Xero for 
          the previous month, at the end of the month in GBP.

      - name: previous_month_eom_xero_operator_net_fees_in_gbp
        data_type: numeric
        description: |
          Net fees for operators processed through Xero for the previous month, 
          at the end of the month in GBP.

      - name: previous_month_eom_xero_waiver_paid_back_to_host_in_gbp
        data_type: numeric
        description: |
          Waiver amounts paid back to hosts via Xero for the previous month, 
          at the end of the month in GBP.

      - name: previous_month_eom_total_revenue_churn_12m
        data_type: numeric
        description: |
          Total revenue in the last 12 months that was generated by 
          deals that have churned in the previous month, at the end of 
          the month, in GBP.

      - name: previous_month_eom_total_revenue_global_12m
        data_type: numeric
        description: |
          Total revenue generated globally in the last 12 months 
          for the previous month, at the end of the month.
          This is only used to compute the churn rate.

      - name: previous_month_eom_total_revenue_churn_rate
        data_type: numeric
        description: |
          Total revenue churn rate for the previous month, 
          at the end of the month.

      - name: previous_month_eom_booking_net_fees_in_gbp
        data_type: numeric
        description: |
          Total booking net fees in gbp for the previous 
          month at the end of the month.

      - name: previous_month_eom_booking_net_fees_per_billable_booking
        data_type: numeric
        description: |
          Total booking net fees per billable booking for the 
          previous month at the end of the month.

  - name: int_ytd_mtd_aggregated_main_metrics_overview
    description: |
      This model provides a high-level overview of the main metrics for the month-to-date 
      and financial year-to-date periods. Data is aggregated at metric level, and provides 
      evolutions current month MTD vs. previous month EOM, current month MTD vs. previous 
      year MTD and current YTD vs. previous YTD.

    data_tests:
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - date
            - dimension
            - dimension_value
            - metric_name
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - date
            - dimension
            - dimension_value
            - id_metric

    columns:
      - name: date
        data_type: date
        description: The date for the month-to-date and year-to-date metrics.
        data_tests:
          - not_null

      - name: dimension
        data_type: string
        description: The dimension or granularity of the metrics.
        data_tests:
          - accepted_values:
              values:
                - global

      - name: dimension_value
        data_type: string
        description: The value or segment available for the selected dimension.
        data_tests:
          - not_null

      - name: calendar_year
        data_type: integer
        description: The calendar year associated with the data.
        data_tests:
          - not_null

      - name: financial_year
        data_type: integer
        description: The financial year associated with the data.
        data_tests:
          - not_null

      - name: previous_year_date
        data_type: date
        description: |
          The equivalent date in the previous year. It can be null if the 
          metric is not available in the previous year

      - name: id_metric
        data_type: integer
        description: |
          Unique ID for this metric. It is preferable to use this ID when 
          building a report to ensure changes in the metric name do not
          affect the report.
        data_tests:
          - not_null

      - name: metric_name
        data_type: string
        description: |
          Name of the metric. It is preferable to use the ID of the metric
          when building a report to ensure changes in the metric name do not
          affect the report.
        data_tests:
          - not_null

      - name: number_format
        data_type: string
        description: |
          Number format to display the metric in the report.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - INTEGER
                - PERCENTAGE_2_DECIMALS
                - CURRENCY_GBP_INTEGER
                - CURRENCY_GBP_1_DECIMAL

      - name: display_exclusion
        data_type: string
        description: |
          Category to indicate if the metric requires a certain exclusion due 
          to relying on not timely information.
          This will limit the display for reporting purposes.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - NONE
                - INVOICING
                - ONGOING_MONTH

      - name: current_month_mtd
        data_type: numeric
        description: |
          Value of the metric for the current month MTD.

      - name: previous_month_eom
        data_type: numeric
        description: |
          Value of the metric for the previous month EOM.

      - name: previous_year_mtd
        data_type: numeric
        description: |
          Value of the metric for the previous year MTD.

      - name: current_year_ytd
        data_type: numeric
        description: |
          Value of the metric for the current year YTD.

      - name: previous_year_ytd
        data_type: numeric
        description: |
          Value of the metric for the previous year YTD.

      - name: diff_current_month_mtd_vs_previous_month_eom
        data_type: numeric
        description: |
          Difference between the current month MTD and the previous month EOM.

      - name: diff_current_month_mtd_vs_previous_year_mtd
        data_type: numeric
        description: |
          Difference between the current month MTD and the previous year MTD.

      - name: diff_current_ytd_vs_previous_ytd
        data_type: numeric
        description: |
          Difference between the current year YTD and the previous year YTD.

      - name: rel_diff_current_month_mtd_vs_previous_month_eom
        data_type: numeric
        description: |
          Relative difference between the current month MTD and the previous month EOM.

      - name: rel_diff_current_month_mtd_vs_previous_year_mtd
        data_type: numeric
        description: |
          Relative difference between the current month MTD and the previous year MTD.

      - name: rel_diff_current_ytd_vs_previous_ytd
        data_type: numeric
        description: |
          Relative difference between the current year YTD and the previous year YTD.

      - name: target_eom_value
        data_type: numeric
        description: |
          The EOM target value for this metric. This is the value that we aim to 
          achieve by the end of the month. It can be null if the target is not 
          available.

      - name: target_ytd_value
        data_type: numeric
        description: |
          The YTD target value for this metric. This is the cumulative value that we
          aim to achieve by the end of each month with respect to the beginning of the 
          financial year, that will put us to reach the EOFY target. It can be null if
          the target is not available.

      - name: target_eofy_value
        data_type: numeric
        description: |
          The EOFY target value for this metric. This is the value that we aim to 
          achieve by the end of the financial year. It can be null if the target is
          not available.

      - name: diff_current_month_mtd_vs_eom_target
        data_type: numeric
        description: |
          Difference between the current month MTD and the EOM target. It can be null
          if the target is not available.

      - name: diff_current_ytd_vs_ytd_target
        data_type: numeric
        description: |
          Difference between the current year YTD and the YTD target. It can be null
          if the target is not available.

      - name: diff_current_ytd_vs_eofy_target
        data_type: numeric
        description: |
          Difference between the current year YTD and the EOFY target. It can be null
          if the target is not available.

      - name: rel_diff_current_month_mtd_vs_eom_target
        data_type: numeric
        description: |
          Relative difference between the current month MTD and the EOM target. It can be null
          if the target is not available.

      - name: rel_diff_current_ytd_vs_ytd_target
        data_type: numeric
        description: |
          Relative difference between the current year YTD and the YTD target. It can be null
          if the target is not available.

      - name: achievement_rate_current_ytd_vs_eofy_target
        data_type: numeric
        description: |
          Achievement rate between the current year YTD and the EOFY target. It can be null
          if the target is not available.

      - name: rel_diff_with_sign_current_month_mtd_vs_previous_month_eom
        data_type: numeric
        description: |
          Relative difference between the current month MTD and the previous month EOM, 
          with a sign to represent if the relative difference is good (positive) or bad 
          (negative) for our business.

      - name: rel_diff_with_sign_current_month_mtd_vs_previous_year_mtd
        data_type: numeric
        description: |
          Relative difference between the current month MTD and the previous year MTD, 
          with a sign to represent if the relative difference is good (positive) or bad 
          (negative) for our business.

      - name: rel_diff_with_sign_current_ytd_vs_previous_ytd
        data_type: numeric
        description: |
          Relative difference between the current year YTD and the previous year YTD, 
          with a sign to represent if the relative difference is good (positive) or bad 
          (negative) for our business.

      - name: rel_diff_with_sign_current_month_mtd_vs_eom_target
        data_type: numeric
        description: |
          Relative difference between the current month MTD and the EOM target, 
          with a sign to represent if the relative difference is good (positive) or bad 
          (negative) for our business.

      - name: rel_diff_with_sign_current_ytd_vs_ytd_target
        data_type: numeric
        description: |
          Relative difference between the current year YTD and the YTD target, 
          with a sign to represent if the relative difference is good (positive) or bad 
          (negative) for our business.

  - name: int_mtd_aggregated_metrics_by_deal
    description: |
      This model aggregates the historic information of our business by providing
      different metrics at account level (by id_deal).
      Additionally it provides Deal attributes.
      Metrics displayed in the model range for the past 24 months. Also, churned 
      accounts are available for historical values, until 3 months after the offboarding 
      date.

    data_tests:
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - date
            - metric
            - id_deal
    columns:
      - name: year
        data_type: int
        description: Year number of the given date.

      - name: month
        data_type: int
        description: Month number of the given date.

      - name: day
        data_type: int
        description: Day monthly number of the given date.

      - name: is_end_of_month
        data_type: boolean
        description: Is end of month, 1 for yes, 0 for no.

      - name: is_current_month
        data_type: boolean
        description: |
          Checks if the date is within the current executed month, 
          1 for yes, 0 for no.

      - name: is_end_of_month_or_yesterday
        data_type: boolean
        description: |
          Checks if the date is end of month or yesterday, 
          1 for yes, 0 for no.

      - name: first_day_month
        data_type: date
        description: |
          First day of the month corresponding to the date field. 
          It comes from int_dates_mtd logic.

      - name: date
        data_type: date
        description: |
          Main date for the computation, that is used for filters. 
          It comes from int_dates_mtd logic.
        data_tests:
          - not_null

      - name: id_deal
        data_type: string
        description: |
          Unique ID for a deal, or account.
        data_tests:
          - not_null

      - name: deal
        data_type: string
        description: |
          Combination of the ID and the Name of the deal.

      - name: active_accommodations_per_deal_segmentation
        data_type: string
        description: |
          Segment value based on the number of listings booked in 12 months 
          for a given deal and date.

      - name: main_billing_country_iso_3_per_deal
        data_type: string
        description: |
          ISO 3166-1 alpha-3 main country code in which the Deal is billed.
          In some cases it's null.

      - name: business_scope
        data_type: string
        description: |
          Business scope identifying the metric source.

      - name: account_manager
        data_type: string
        description: |
          Account manager of the deal.
          In some cases it's null.

      - name: deal_lifecycle_state
        data_type: string
        description: |
          Lifecycle state of the deal.

      - name: metric
        data_type: text
        description: Name of the business metric.
        data_tests:
          - not_null

      - name: order_by
        data_type: integer
        description: |
          Order for displaying purposes. Null values are accepted, but keep
          in mind that then there's no default controlled display order.

      - name: number_format
        data_type: text
        description: Allows for grouping and formatting for displaying purposes.

      - name: value
        data_type: numeric
        description: |
          Numeric value (integer or decimal) that corresponds to the MTD computation of the metric
          at a given date. Note that if the month is not in progress, then this value corresponds
          to the monthly figure.

      - name: previous_year_value
        data_type: numeric
        description: |
          Numeric value (integer or decimal) that corresponds to the MTD computation of the metric
          on the previous year at a given date.

      - name: relative_increment
        data_type: numeric
        description: |
          Numeric value that corresponds to the relative increment between value and previous year value,
          following the computation: value / previous_year_value - 1.

      - name: relative_increment_with_sign_format
        data_type: numeric
        description: |
          Relative_increment value multiplied by -1 in case this metric's growth doesn't have a
          positive impact for Truvi, otherwise is equal to relative_increment.
          This value is specially created for formatting in PBI

      - name: display_exclusion
        data_type: string
        description: |
          Category to indicate if the metric requires a certain exclusion due 
          to relying on not timely information.
          This will limit the display for reporting purposes.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - NONE
                - INVOICING
                - ONGOING_MONTH

  - name: int_flagging_booking_categorisation
    description: |
      A model that computes different Booking counts depending whether these 
      had claims or not, if these were categorised at risk or not, and if there 
      was a submitted payout or not. 
      This only applies for Bookings: 
      - that come from New Dash users 
      - that are protected, either by a protection or a deposit management service 

      Additionally, we track Completed Bookings as those Bookings which, as of today, 
      have been checked out for more than natural 14 days.

      From these Bookings, we check if these had an incident related in Resolution 
      Center:
      - that is linked to a Booking 
      - that is not in a duplicated status 

      Since Bookings can be duplicated in the incidents data, we effectively consider:
      - Bookings with "any" claim 
      - Bookings with a finished claim, either with a payout or not
      - Bookings with a finished claim and a submitted amount for payout

    data_tests:
      - dbt_expectations.expect_table_row_count_to_equal:
          value: 1
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: total_bookings
          column_B: completed_bookings + not_completed_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: total_with_claim_bookings
          column_B: completed_with_claim_bookings + not_completed_with_claim_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_bookings
          column_B: completed_with_claim_bookings + completed_without_claim_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_bookings
          column_B: completed_risk_bookings + completed_no_risk_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_risk_bookings
          column_B: completed_risk_with_claim_bookings + completed_risk_without_claim_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_with_claim_bookings
          column_B: completed_risk_with_claim_bookings + completed_no_risk_with_claim_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_no_risk_bookings
          column_B: completed_no_risk_with_claim_bookings + completed_no_risk_without_claim_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_without_claim_bookings
          column_B: completed_risk_without_claim_bookings + completed_no_risk_without_claim_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_bookings
          column_B: completed_awaiting_resolution_bookings + completed_not_awaiting_resolution_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_not_awaiting_resolution_bookings
          column_B: completed_with_submitted_payout_bookings + completed_without_submitted_payout_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_with_submitted_payout_bookings
          column_B: completed_risk_with_submitted_payout_bookings + completed_no_risk_with_submitted_payout_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_without_submitted_payout_bookings
          column_B: completed_risk_without_submitted_payout_bookings + completed_no_risk_without_submitted_payout_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_bookings
          column_B: completed_risk_with_claim_bookings + completed_no_risk_without_claim_bookings + completed_risk_without_claim_bookings + completed_no_risk_with_claim_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_not_awaiting_resolution_bookings
          column_B: completed_risk_with_submitted_payout_bookings + completed_no_risk_without_submitted_payout_bookings + completed_risk_without_submitted_payout_bookings + completed_no_risk_with_submitted_payout_bookings
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: total_amount_paid_in_gbp
          column_B: completed_amount_paid_in_gbp + not_completed_amount_paid_in_gbp
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_amount_paid_in_gbp
          column_B: completed_finished_incidents_amount_paid_in_gbp + completed_awaiting_finish_incidents_amount_paid_in_gbp
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: completed_finished_incidents_amount_paid_in_gbp
          column_B: completed_risk_with_submitted_payout_amount_paid_in_gbp + completed_no_risk_with_submitted_payout_amount_paid_in_gbp

    columns:
      - name: total_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected Bookings, either a Protection Service 
          or a Deposit Management service, for reference.

      - name: completed_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected Bookings with a Checkout happening 
          more than 14 days ago.

      - name: not_completed_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected Bookings with a Checkout happening 
          between 14 days ago and today, or in the future.

      - name: total_with_claim_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected Bookings that have had a claim, 
          indistinctly of these bookings being considered as completed or not.

      - name: completed_with_claim_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          had a claim.

      - name: not_completed_with_claim_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected, NOT Completed Bookings that have 
          had a claim.

      - name: completed_without_claim_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          NOT had a claim.

      - name: completed_risk_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          been flagged as at Risk.

      - name: completed_no_risk_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          NOT been flagged as at Risk.

      - name: completed_awaiting_resolution_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have
          a claim and are in a resolution status that is not finished. These 
          Bookings are excluded for the submitted payout-based performance
          analysis, as we don't know if the claim will be paid out or not.

      - name: completed_not_awaiting_resolution_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that are 
          not awaiting resolution, either because they have a claim in a finished
          status or because they don't have a claim at all.

      - name: completed_with_submitted_payout_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          had a submitted payout, with the claim being in a finished status.

      - name: completed_without_submitted_payout_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          NOT had a submitted payout, either because there's a claim being in 
          a finished status without a payout or because there's no claim at all.

      - name: completed_risk_with_claim_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          been flagged as at Risk AND that have had a claim. 
          For the claim-based performance analysis, this would be the true positive.

      - name: completed_no_risk_without_claim_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          NOT been flagged as at Risk AND that have NOT had a claim. 
          For the claim-based performance analysis, this would be the true negative.

      - name: completed_risk_without_claim_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          been flagged as at Risk AND that have NOT had a claim. 
          For the claim-based performance analysis, this would be the false positive.

      - name: completed_no_risk_with_claim_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          NOT been flagged as at Risk AND that have had a claim. 
          For the claim-based performance analysis, this would be the false negative.

      - name: completed_risk_with_submitted_payout_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          been flagged as at Risk AND that have had a submitted payout, with 
          the claim being in a finished status.
          For the submitted payout-based performance analysis, this would be 
          the true positive.

      - name: completed_no_risk_without_submitted_payout_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          NOT been flagged as at Risk AND that have NOT had a submitted payout, 
          either because there's a claim being in a finished status without a 
          payout or because there's no claim at all.  
          For the submitted payout-based performance analysis, this would be 
          the true negative.

      - name: completed_risk_without_submitted_payout_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          been flagged as at Risk AND that have NOT had a submitted payout, 
          either because there's a claim being in a finished status without a 
          payout or because there's no claim at all.  
          For the submitted payout-based performance analysis, this would be 
          the false positive.

      - name: completed_no_risk_with_submitted_payout_bookings
        data_type: integer
        description: |
          Current count of New Dash Protected and Completed Bookings that have 
          NOT been flagged as at Risk AND that have had a submitted payout, with 
          the claim being in a finished status.
          For the submitted payout-based performance analysis, this would be 
          the false negative.

      - name: total_amount_paid_in_gbp
        data_type: numeric
        description: |
          Total amount paid in GBP in terms of Resolutions Payouts for all 
          Bookings.

      - name: completed_amount_paid_in_gbp
        data_type: numeric
        description: |
          Total amount paid in GBP in terms of Resolutions Payouts for Completed 
          Bookings.

      - name: not_completed_amount_paid_in_gbp
        data_type: numeric
        description: |
          Total amount paid in GBP in terms of Resolutions Payouts for 
          Bookings that are not completed, for reference.

      - name: completed_finished_incidents_amount_paid_in_gbp
        data_type: numeric
        description: |
          Total amount paid in GBP in terms of Resolutions Payouts for Completed 
          Bookings that have had a claim in a finished status.

      - name: completed_awaiting_finish_incidents_amount_paid_in_gbp
        data_type: numeric
        description: |
          Total amount paid in GBP in terms of Resolutions Payouts for Completed 
          Bookings that have had a claim in a status that is not finished. These 
          Bookings are excluded for the submitted payout-based performance
          analysis, as technically the incident has not finished so there might 
          be further updates.

      - name: completed_risk_with_submitted_payout_amount_paid_in_gbp
        data_type: numeric
        description: |
          Total amount paid in GBP in terms of Resolutions Payouts for Completed 
          Bookings that have been flagged as at Risk AND that have had a 
          submitted payout, with the claim being in a finished status.

      - name: completed_no_risk_with_submitted_payout_amount_paid_in_gbp
        data_type: numeric
        description: |
          Total amount paid in GBP in terms of Resolutions Payouts for Completed 
          Bookings that have NOT been flagged as at Risk AND that have had a 
          submitted payout, with the claim being in a finished status.

  - name: int_flagging_performance_analysis
    description: |
      Provides a basic statistical analysis with binary classification metrics 
      on the flagging performance for New Dash Protected bookings, in the scope
      of claims raised or submitted payouts.
    data_tests:
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: count_total
          column_B: count_true_positive + count_true_negative + count_false_positive + count_false_negative
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: recall_score
          column_B: 1.0 * count_true_positive / (count_true_positive + count_false_negative)
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: precision_score
          column_B: 1.0 * count_true_positive / (count_true_positive + count_false_positive)
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: false_positive_rate_score
          column_B: 1.0 * count_false_positive / (count_false_positive + count_true_negative)
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: f1_score
          column_B: 2.0 * count_true_positive / (2 * count_true_positive + count_false_negative + count_false_positive)
      - dbt_expectations.expect_column_pair_values_to_be_equal:
          column_A: f2_score
          column_B: 5.0 * count_true_positive / (5 * count_true_positive + 4 * count_false_negative  + count_false_positive)

    columns:
      - name: flagging_analysis_type
        data_type: string
        description: |
          Type of the analysis conducted, i.e., what do we consider as a 
          positive - predicted (flagged) vs. actual (claim, payout).
        data_tests:
          - not_null
          - unique
          - accepted_values:
              values:
                - RISK_VS_CLAIM
                - RISK_VS_SUBMITTED_PAYOUT

      - name: count_total
        data_type: integer
        description: |
          Total count of bookings considered for the flagging performance analysis.

      - name: count_true_positive
        data_type: integer
        description: |
          Count of True Positives: predicted positives that are also an actual positive.

      - name: count_true_negative
        data_type: integer
        description: |
          Count of True Negatives: predicted negatives that are also an actual negative.

      - name: count_false_positive
        data_type: integer
        description: |
          Count of False Positives: predicted positives that are not an actual positive.

      - name: count_false_negative
        data_type: integer
        description: |
          Count of False Negatives: predicted negatives that are not an actual negative.

      - name: true_positive_score
        data_type: decimal
        description: |
          True Positives as a ratio over 1. This is the count of true positives divided 
          by the total count of bookings considered for the flagging performance analysis.

      - name: true_negative_score
        data_type: decimal
        description: |
          True Negatives, as a ratio over 1. This is the count of true negatives divided
          by the total count of bookings considered for the flagging performance analysis.

      - name: false_positive_score
        data_type: decimal
        description: |
          False Positives, as a ratio over 1. This is the count of false positives divided
          by the total count of bookings considered for the flagging performance analysis.

      - name: false_negative_score
        data_type: decimal
        description: |
          False Negative, as a ratio over 1. This is the count of false negatives divided
          by the total count of bookings considered for the flagging performance analysis.

      - name: recall_score
        data_type: decimal
        description: |
          Recall score, or true positive rate. This corresponds to the proportion of all
          actual positives that were classified correctly as a positive. It can be seen 
          as a probability of detection: in our case, it answers the question "what 
          fraction of claim/payouts were flagged as at risk?".
          This is the count of true positives divided by the sum of true positives and 
          false negatives. Recall improves when false negatives decrease.
          A hypothetical perfect model would have zero false negatives, and thus a 
          recall of 1.0, or 100% detection rate.

      - name: precision_score
        data_type: decimal
        description: |
          Precision score, or positive predictive value. This corresponds to the 
          proportion of all predicted positives that were classified correctly as a 
          positive. In our case, it answers the question "what fraction of Bookings 
          flagged as at Risk actually generated a Claim/Payout?".
          This is the count of true positives divided by the sum of true positives and 
          false positives. Precision improves when false positives decrease.
          A hypothetical perfect model would have zero false positives, and thus a 
          precision of 1.0, or 100% precision rate.

      - name: false_positive_rate_score
        data_type: decimal
        description: |
          False positive rate, or fall-out. This corresponds to the proportion of all
          actual negatives that were classified incorrectly as a positive. It can be seen 
          as a probability of false alarm: in our case, it answers the question "what
          fraction of bookings without a claim/payout were flagged as at risk?".
          This is the count of false positives divided by the sum of true positives and 
          false positives.
          A hypothetical perfect model would have zero false positives, and thus a 
          false positive rate of 0.0, or 0% false alarm rate.

      - name: f1_score
        data_type: decimal
        description: |
          F1 score, which computes the harmonic mean of precision and recall.
          This metric balances the trade-off between precision and recall, and is useful 
          when we want to find an optimal balance between the two.
          It is defined as 2 * (precision * recall) / (precision + recall).
          A hypothetical perfect model would have an F1 score of 1.0, or 100%.
          When precision and recall are far apart, the F1 score will be closer to the 
          lower of the two.

      - name: f2_score
        data_type: decimal
        description: |
          F2 score, which computes the harmonic mean of precision and recall, but 
          with a twice higher weight on recall. In our case, it effectively means 
          that we want to reduce the number of false negatives, meaning reducing 
          the number of claims/payouts that are not flagged as at risk, while still 
          keeping a good precision.
          This metric is useful when we want to prioritize recall over precision, 
          and is defined as 5 * (precision * recall) / (4 * precision + recall).
          A hypothetical perfect model would have an F2 score of 1.0, or 100%.
          When precision and recall are far apart, the F2 score will be closer to the 
          lower of the two.

  - name: int_billable_items_growth_score_by_deal
    description: |
      This model computes the growth score of the billable items for each deal.
      The growth score is computed as the average between: 
        - The billable items of a given month vs. the average of the previous
          3 months.
        - The share a deal has in terms of billable items of a given month 
          if compared to the rest of the deals vs. the average of the previous 3 
          months.
      The growth score is capped between -1 and 1. 
      It is important to note that if we check the current month, the count of 
      billable items and the corresponding share will be based on the projection,
      rather than the actual figure. In this case, the MAE and MAPE of the projected 
      value are indicated in the model.
      While the growth score is computed at a monthly basis, the value will update 
      every day with the latest available projection.

    data_tests:
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - end_date
            - id_deal
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - start_date
            - id_deal

    columns:
      - name: start_date
        data_type: date
        description: |
          Start date of the period for which the growth score is computed.
        data_tests:
          - not_null

      - name: end_date
        data_type: date
        description: |
          End date of the period for which the growth score is computed.
        data_tests:
          - not_null

      - name: id_deal
        data_type: string
        description: |
          Unique ID for a deal, or account.
        data_tests:
          - not_null

      - name: current_month_billable_items
        data_type: integer
        description: |
          Monthly billable items. If the month is in progress 
          then this value corresponds to the projected figure.
          This is indicated by "are_billable_items_projected"
          flag.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: prior_3_months_avg_monthly_billable_items
        data_type: integer
        description: |
          Average of the billable items for the previous 3 months.
          If the selected range is from 1st April 2025 to 30th April 2025,
          then this average will be based between 1st January 2025 to 
          31st March 2025.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: current_month_share_billable_items
        data_type: decimal
        description: |
          Share of the billable items for a given deal in the current month.
          If the month is in progress then this value corresponds to the 
          projected figure. This is indicated by "are_billable_items_projected"
          flag.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: prior_3_months_avg_monthly_share_billable_items
        data_type: decimal
        description: |
          Average of the share of the billable items for a given deal in the 
          previous 3 months. If the selected range is from 1st April 2025 to 
          30th April 2025, then this average will be based between 1st January 
          2025 to 31st March 2025.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: growth_vs_prior_3_avg_billable_items
        data_type: decimal
        description: |
          Growth score of the billable items based purely on the relative 
          difference between the current month billable items vs. the
          prior 3 months average.
          This is a subcomputation of the growth score, for information 
          purposes.
          It can be null.

      - name: growth_vs_prior_3_avg_share_billable_items
        data_type: decimal
        description: |
          Growth score of the billable items based purely on the relative 
          difference between the current month share billable items vs. the
          prior 3 months average.
          This is a subcomputation of the growth score, for information 
          purposes.
          It can be null.

      - name: growth_score
        data_type: decimal
        description: |
          Growth score of the billable items, based on the average between:
            - The billable items of a given month vs. the average of the previous
              3 months.
            - The share a deal has in terms of billable items of a given month 
              if compared to the rest of the deals vs. the average of the previous 3 
              months.
          The growth score is capped between -1 and 1.
          It cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: -1
              max_value: 1
              strictly: false

      - name: projection_mean_absolute_error
        data_type: decimal
        description: |
          Mean absolute error of the projection of the billable items.
          It is null if the month is not in progress or value is projected 
          but there's no prior data to compare the projection against.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: projection_mean_absolute_percentage_error
        data_type: decimal
        description: |
          Mean absolute percentage error of the projection of the billable items.
          It is null if the month is not in progress or value is projected 
          but there's no prior data to compare the projection against.
        data_tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              strictly: false

      - name: are_billable_items_projected
        data_type: boolean
        description: |
          Flag indicating if the billable items are projected or not.
          If the month is in progress then this value corresponds to the 
          projected figure. This is indicated by "are_billable_items_projected"
          flag.
        data_tests:
          - not_null

  - name: int_unified_api_verifications
    description: |
      This model unifies the API verifications data from different sources 
      (Athena, E-Deposit, Check In Hero, Screen & Protect) into a single table. 
      It also includes information regarding the Booking. 
      Since the data is coming from different sources, the model provides a 
      minimal set of columns that are relevant for this unified view.
    data_tests:
      - dbt_utils.unique_combination_of_columns:
          combination_of_columns:
            - id_verification
            - api_source

    columns:
      - name: api_source
        data_type: string
        description: |
          Source of the API verification (e.g., ATHENA, E-DEPOSIT, CHECK_IN_HERO,
          SCREEN_AND_PROTECT). This field is mandatory and cannot be null.
        data_tests:
          - not_null
          - accepted_values:
              values:
                - ATHENA
                - E-DEPOSIT
                - CHECK_IN_HERO
                - SCREEN_AND_PROTECT

      - name: id_verification
        data_type: string
        description: |
          Unique ID for a verification. This ID is generated by each API, 
          thus it can potentially be duplicated across different sources.
        data_tests:
          - not_null

      - name: id_booking
        data_type: string
        description: |
          Unique ID for a booking. It can be duplicated, as 
          reflected in the field "is_duplicate_booking".
        data_tests:
          - not_null

      - name: id_deal
        data_type: string
        description: |
          Unique ID for a deal, or account.
        data_tests:
          - not_null

      - name: verification_status
        data_type: string
        description: |
          Status of the verification. It can be null 
          for some sources. Status depends on the source.

      - name: check_in_date_utc
        data_type: date
        description: |
          Check-in date in UTC. It cannot be null.
        data_tests:
          - not_null

      - name: check_out_date_utc
        data_type: date
        description: |
          Check-out date in UTC. It cannot be null.
        data_tests:
          - not_null

      - name: created_date_utc
        data_type: date
        description: |
          Created date in UTC. It cannot be null.
        data_tests:
          - not_null

      - name: billable_date_utc
        data_type: date
        description: |
          Billable date in UTC. This is the point in time in 
          which the verification can be billed. It cannot be null.
        data_tests:
          - not_null

      - name: is_cancelled
        data_type: boolean
        description: |
          Flag indicating if the booking linked to the verification 
          is cancelled or not. It can be null for some sources.

      - name: is_duplicate_booking
        data_type: boolean
        description: |
          Flag indicating if the booking linked to the verification 
          is a duplicate or not. It cannot be null.
        data_tests:
          - not_null

      - name: booking_is_duplicated_n_times
        data_type: integer
        description: |
          Number of times the booking linked to the verification 
          is duplicated. It cannot be null.
        data_tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 1
              strictly: false