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).
        tests:
          - not_null
          - unique
      - name: from_currency
        data_type: character
        description: The source currency, represented as an ISO 4217 code.
        tests:
          - not_null
      - name: to_currency
        data_type: character
        description: The target currency, represented as an ISO 4217 code.
        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.
        tests:
          - not_negative_or_zero
          - not_null
      - name: rate_date_utc
        data_type: date
        description: The date in which the rate record is relevant.
        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.
        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.
        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.
    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.
        tests:
          - not_null
      - name: to_currency
        data_type: character
        description: The source currency, represented as an ISO 4217 code.
        tests:
          - not_null
      - name: rate
        data_type: numeric
        description: The target currency, represented as an ISO 4217 code.
        tests:
          - not_null
      - name: rate_date_utc
        data_type: date
        description: The date in which the rate record is relevant.
        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.
        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

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

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

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

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

    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.
        tests:
          - not_null

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

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

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

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

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

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

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

    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.
        tests:
          - not_null

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

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

      - 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
    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.

    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.
        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.
        tests:
          - not_null

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

      - 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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        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.
        tests:
          - not_null
          - accepted_values:
              values:
                - MAJOR DECLINE
                - DECLINE
                - FLAT
                - GAIN
                - MAJOR GAIN
                - UNSET

  - name: int_monthly_12m_window_contribution_by_deal
    description: |
      The main goal of this model is to provide how much a deal 
      contributes to a given metric on the global amount over a 
      period of 12 months.

      At the moment, this is only done for 3 metrics:
      - total_revenue_in_gbp
      - created_bookings 
      - listings_booked_in_month 

      The contribution is based on an Average approach:
        Over a period of 12 months, sum the value of a given a metric 
        for each deal, and divide it by the amount of months we're considering
        for that deal. Sum all the average amounts per deals to get a global. 
        Divide the avg per deal value vs. the sum of avgs global one.
      The average approach "boosts" the contribution of those accounts 
      that have been active for less than 12 months.

    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. 
          Metrics are inclusive to this date. Together with id_deal, it 
          acts as the primary key of this model.
        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.
        tests:
          - not_null

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

      - name: preceding_months_count_by_deal
        data_type: integer
        description: |
          Number of months preceding to the one given by date 
          that are used for the historic metric retrieval for 
          a given deal. In essence it states the amount of 
          months a given deal has been active before a the month 
          given by date, capped at 12 months.
        tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              max_value: 12
              strictly: false

      - name: has_deal_been_created_less_than_12_months_ago
        data_type: boolean
        description: |
          Flag to identify if a given deal has been created less
          than 12 months ago (true) or not (false). It's based on the
          preceding_months_count_by_deal, and will be true on the first
          year of deal activity.

      - name: total_revenue_12m_average_contribution
        data_type: numeric
        description: |
          Share of the deal contribution on total revenue 
          vs. the global amount, on the preceding 12 months 
          with respect to date. It uses the average approach.
          It can be negative.
        tests:
          - not_null

      - name: created_bookings_12m_average_contribution
        data_type: numeric
        description: |
          Share of the deal contribution on created bookings 
          vs. the global amount, on the preceding 12 months 
          with respect to date. It uses the average approach.
        tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              max_value: 1
              strictly: false

      - name: listings_booked_in_month_12m_average_contribution
        data_type: numeric
        description: |
          Share of the deal contribution on listings booked in month 
          vs. the global amount, on the preceding 12 months 
          with respect to date. It uses the average approach.
        tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              max_value: 1
              strictly: false

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

      It computes the churn contribution by dimension, dimension value 
      and date, in a monthly basis. This model is different from the 
      usual mtd ones since it strictly depends on the monthly computation 
      of metrics by deal, which is done in a monthly basis rather than mtd. 
      In essence, it means we won't have data for the current month. 

      This model retrieves the 12 month contribution to global metrics 
      by deal and aggregates it to dimension and dimension value for those 
      deals that are tagged as '05-Churning' in that month. Thus, it provides 
      a total of 3 churn related metrics, represented as ratios over the total:
      - Total Revenue (in GBP)
      - Created Bookings 
      - Listings Booked in Month 
      by using the Average contribution method. For further 
      information, please refer to the documentation of the model: 
      - int_monthly_12m_window_contribution_by_deal

      Lastly, when checking data at any dimension distinct from Global, at the 
      moment these values represent the additive contribution of churn with respect 
      to the global amount. This means that, for instance, if we have 10% of churn 
      in a month, it can be divided by 9% USA and 1% GBR since 9%+1% = 10%.

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

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

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

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

      - name: total_revenue_churn_average_contribution
        data_type: numeric
        description: Total Revenue churn rate (average approach).

      - name: created_bookings_churn_average_contribution
        data_type: numeric
        description: Created Bookings churn rate (average approach).

      - name: listings_booked_in_month_churn_average_contribution
        data_type: numeric
        description: Listings Booked in Month churn rate (average approach).

  - 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"
        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"
        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)"
        tests:
          - accepted_values:
              values:
                - V1
                - V2

      - name: verification_source
        data_type: text
        description: "source of the verification for the booking"
        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 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 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_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"