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version : 2
models :
- name : int_core__duplicate_bookings
description : |
A list of bookings which are considered duplicates of other bookings.
We currently consider two bookings to be duplicate if they have the same :
- Guest user id
- Accomodation id
- Check-in date
Bear in mind these bookings do have different booking ids.
Out of a duplicated tuple of 2 or more bookings :
- Our logic will consider the oldest one to be the "original", not duplicate one.
- This table will contain only the duplicates, and not the original.
columns :
- name : id_booking
data_type : bigint
description : The unique, Superhog generated id for this booking.
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tests :
- unique
- not_null
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- name : is_duplicate_booking
data_type : boolean
description : |
True if the booking is duplicate.
If you are thinking that this is redundant, you are right. All
records in this table will be true. But we keep this field to
make your life easier when joining with other tables.
- name : is_duplicating_booking_with_id
data_type : bigint
description : |
Indicates what's the original booking being duplicated.
If there is a tuple of duplicate bookings {A, B, C}, where A is the
original and the others are the duplicates :
- B and C will appear in this table, A will not.
- The value of this field for both B and C will be A's id.
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tests :
- not_null
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- name : int_core__booking_charge_events
description : |
Booking charge events is a fancy word for saying : a booking happened,
the related host had a booking fee set up at the right time, hence we
need to charge him.
The table contains one record per booking and shows the associated
booking fee, as well as the point in time in which the charge event was
considered.
Be wary of the booking fees : they don't have an associated currency.
Crazy, I know, but we currently don't store that information in the
backend.
As for the charge dates : the exact point in time at which we consider
that we should be charging a fee depends on billing details of the host
customer. For some bookings, this will be the check-in. For others, its
when the guest begins the verification process.
Not all bookings appear here since we don't charge a fee for all
bookings.
columns :
- name : id_booking
data_type : bigint
description : The unique, Superhog generated id for this booking.
- name : id_price_plan
data_type : bigint
description : The id of the price plan that relates to this booking.
- name : booking_fee_local
data_type : numeric
description : The fee to apply to the booking, in host currency.
- name : booking_fee_charge_at_utc
data_type : timestamp without time zone
description : |
The point in time in which the booking should be invoiced.
This could be the check-in date of the booking or the date in which the guest verification
started, depending on the billing settings of the host.
- name : booking_fee_charge_date_utc
data_type : date
description : |
The date in which the booking should be invoiced.
This could be the check-in date of the booking or the date in which the guest verification
started, depending on the billing settings of the host.
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- name : int_core__check_in_cover_prices
description : |
This table shows the active price and cover for the Check-In Hero
product.
The prices are obtained through a gross `GROUP BY` thrown at the payment
validation sets table. It works this way because the price settings of
this product were done with a terrible backend data model design.
How could the prices be changed remains a mystery, and the current design
does not support any kind of history tracking. When the time comes to
adjust prices, we will have a lot of careful work to do to make sure that
we keep history and that no downstream dependencies of this model blow
up.
columns :
- name : local_currency_iso_4217
data_type : character varying
description : A currency code.
- name : checkin_cover_guest_fee_local_curr
data_type : numeric
description : |
The fee that the guest user must pay if he wants to purchase the
cover.
- name : checkin_cover_cover_amount_local_curr
data_type : numeric
description : |
The amount for which the guest user is covered if he faces problems
during check-in.
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- name : int_core__unified_user
columns :
- name : id_user
data_type : character varying
description : The unique ID for the user.
tests :
- not_null
- unique
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- name : int_core__vr_check_in_cover
columns :
- name : id_verification_request
data_type : character varying
description : The unique ID for the verification request.
tests :
- not_null
- unique
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- name : int_core__monthly_accommodation_history_by_deal
description : |
This model contains the historic information regarding the accommodations, also known
as listings, for each deal id. It's used for the business KPIs in the view by deal id.
Data is aggregated at the last day of the month, or up to yesterday if it's the
current month.
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 listing metrics.
tests :
- not_null
- name : id_deal
data_type : character varying
description : Id of the deal associated to the host.
tests :
- not_null
Merged PR 2164: Adding booking metrics by deal id for business kpis
This is a first approach to compute some easy metrics for the "deal" based business kpis. At this stage, it contains the information of bookings (created, checkout, cancelled) per deal and month, including both historic months as well as the current one. This do not contain MTD computation because it's overkill to do a MTD at deal level (+ we have 1k deals, so scalability can become a problem in the future)
Models:
- **int_dates_by_deal**: simple model that reads from **int_dates** and just joins it with **unified_users** to retrieve the deals. It will be used as the 'source of truth' for which deals should be considered in a given month, basically, since the first host associated to a deal is created (not necessarily booked)
- **int_core__monthly_booking_history_by_deal**: it contains the history of bookings per deal id in a monthly basis. It should be easy enough to integrate here, in the future and if needed, B2B macro segmentation.
In terms of performance, comparing the model **int_core__monthly_booking_history_by_deal** and **int_core__mtd_booking_metrics** you'll see that I removed the joined with the **int_dates_xxx** in the CTEs. This is because I want to avoid a double join of date & deal that I tried and I stopped after 5 min running. Since this computation is in a monthly basis - no MTD - it's easy enough to just apply the **int_dates_by_deal** on the last part of the query. With this approach, it runs in 7 seconds.
Related work items: #17689
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- name : int_core__monthly_booking_history_by_deal
description : |
This model contains the historic information regarding the bookings for each deal id.
It's used for the business KPIs in the view by deal id. Data is aggregated at the last
day of the month, or up to yesterday if it's the current month.
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 booking metrics.
tests :
- not_null
- name : id_deal
data_type : character varying
description : Id of the deal associated to the host.
tests :
- not_null
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- name : int_core__monthly_guest_journey_history_by_deal
description : |
This model contains the historic information regarding the guest journey for each deal id.
It's used for the business KPIs in the view by deal id. Data is aggregated at the last
day of the month, or up to yesterday if it's the current month.
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 guest journey metrics.
tests :
- not_null
- name : id_deal
data_type : character varying
description : Id of the deal associated to the host.
tests :
- not_null
Merged PR 2164: Adding booking metrics by deal id for business kpis
This is a first approach to compute some easy metrics for the "deal" based business kpis. At this stage, it contains the information of bookings (created, checkout, cancelled) per deal and month, including both historic months as well as the current one. This do not contain MTD computation because it's overkill to do a MTD at deal level (+ we have 1k deals, so scalability can become a problem in the future)
Models:
- **int_dates_by_deal**: simple model that reads from **int_dates** and just joins it with **unified_users** to retrieve the deals. It will be used as the 'source of truth' for which deals should be considered in a given month, basically, since the first host associated to a deal is created (not necessarily booked)
- **int_core__monthly_booking_history_by_deal**: it contains the history of bookings per deal id in a monthly basis. It should be easy enough to integrate here, in the future and if needed, B2B macro segmentation.
In terms of performance, comparing the model **int_core__monthly_booking_history_by_deal** and **int_core__mtd_booking_metrics** you'll see that I removed the joined with the **int_dates_xxx** in the CTEs. This is because I want to avoid a double join of date & deal that I tried and I stopped after 5 min running. Since this computation is in a monthly basis - no MTD - it's easy enough to just apply the **int_dates_by_deal** on the last part of the query. With this approach, it runs in 7 seconds.
Related work items: #17689
2024-07-01 16:00:14 +00:00
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- name : int_core__mtd_booking_metrics
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description : |
This model contains the historic information regarding the bookings in an aggregated manner.
It's used for the business KPIs. Data is aggregated at the last day of the month and in the
days necessary for the Month-to-Date computation of the current month.
Merged PR 2519: mtd bookings with 2 dimensions
# Description
This is a first idea of how I'd like to add dimensionality in the KPIs for the mtd models. For the moment, I keep deal_id apart, so I just touch the "mtd" models, that so far only contained "global" metrics.
In this case I include the listing segmentation (0, 1-5, 6-20, etc) in the bookings. To do this, I created 2 new fields: dimension and dimension_values.
I also created a "master" table with `date` - `dimension` - `dimension_value` called `int_dates_mtd_by_dimension`
Important notes:
- I force a hardcode in `int_mtd_vs_previous_year_metrics`. This is to not break production.
- You will notice how repetitive the code is starting to look. My intention with this PR is that we are happy with this approach on the naming, the strategy for joins, etc. If that's ok, next step is going to be doing macros on top. Think of the state of `int_core__mtd_booking_metrics` as the "compiled version" of the macro that should come afterwards.
# Checklist
- [X] The edited models and dependants run properly with production data.
- [X] The edited models are sufficiently documented.
- [X] The edited models contain PK tests, and I've ran and passed them.
- [ ] I have checked for DRY opportunities with other models and docs.
- [X] I've picked the right materialization for the affected models.
# Other
- [ ] Check if a full-refresh is required after this PR is merged.
Related work items: #19325
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tests :
- dbt_utils.unique_combination_of_columns :
combination_of_columns :
- date
- dimension
- dimension_value
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columns :
- name : date
data_type : date
description : The date for the month-to-date booking-related metrics.
tests :
- not_null
Merged PR 2519: mtd bookings with 2 dimensions
# Description
This is a first idea of how I'd like to add dimensionality in the KPIs for the mtd models. For the moment, I keep deal_id apart, so I just touch the "mtd" models, that so far only contained "global" metrics.
In this case I include the listing segmentation (0, 1-5, 6-20, etc) in the bookings. To do this, I created 2 new fields: dimension and dimension_values.
I also created a "master" table with `date` - `dimension` - `dimension_value` called `int_dates_mtd_by_dimension`
Important notes:
- I force a hardcode in `int_mtd_vs_previous_year_metrics`. This is to not break production.
- You will notice how repetitive the code is starting to look. My intention with this PR is that we are happy with this approach on the naming, the strategy for joins, etc. If that's ok, next step is going to be doing macros on top. Think of the state of `int_core__mtd_booking_metrics` as the "compiled version" of the macro that should come afterwards.
# Checklist
- [X] The edited models and dependants run properly with production data.
- [X] The edited models are sufficiently documented.
- [X] The edited models contain PK tests, and I've ran and passed them.
- [ ] I have checked for DRY opportunities with other models and docs.
- [X] I've picked the right materialization for the affected models.
# Other
- [ ] Check if a full-refresh is required after this PR is merged.
Related work items: #19325
2024-08-08 09:11:01 +00:00
- name : dimension
data_type : string
description : The dimension or granularity of the metrics.
tests :
- accepted_values :
values :
- global
- by_number_of_listings
- name : dimension_value
data_type : string
description : The value or segment available for the selected dimension.
tests :
- not_null
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- name : int_core__mtd_guest_journey_metrics
description : |
This model contains the historic information regarding the guest journeys in an aggregated manner.
It's used for the business KPIs. Data is aggregated at the last day of the month and in the
days necessary for the Month-to-Date computation of the current month.
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tests :
- dbt_utils.unique_combination_of_columns :
combination_of_columns :
- date
- dimension
- dimension_value
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columns :
- name : date
data_type : date
description : The date for the month-to-date guest journey-related metrics.
tests :
- not_null
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- name : dimension
data_type : string
description : The dimension or granularity of the metrics.
tests :
- accepted_values :
values :
- global
- by_number_of_listings
- name : dimension_value
data_type : string
description : The value or segment available for the selected dimension.
tests :
- not_null
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- name : int_core__mtd_accommodation_metrics
description : |
This model contains the historic information regarding the accommodations in an aggregated manner.
It's used for the business KPIs. Data is aggregated at the last day of the month and in the
days necessary for the Month-to-Date computation of the current month.
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tests :
- dbt_utils.unique_combination_of_columns :
combination_of_columns :
- date
- dimension
- dimension_value
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columns :
- name : date
data_type : date
description : The date for the month-to-date accommodation-related metrics.
tests :
- not_null
- unique
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- name : int_core__mtd_deal_metrics
description : |
This model contains the historic information regarding the deals in an aggregated manner.
It's used for the business KPIs. Data is aggregated at the last day of the month and in the
days necessary for the Month-to-Date computation of the current month.
columns :
- name : date
data_type : date
description : The date for the month-to-date deal-related metrics.
tests :
- not_null
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- name : dimension
data_type : string
description : The dimension or granularity of the metrics.
tests :
- accepted_values :
values :
- global
- by_number_of_listings
- name : dimension_value
data_type : string
description : The value or segment available for the selected dimension.
tests :
- not_null
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- name : int_core__mtd_guest_payments_metrics
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description : |
This model contains the historic information regarding the guest revenue in an aggregated manner.
It's used for the business KPIs. Data is aggregated at the last day of the month and in the
days necessary for the Month-to-Date computation of the current month.
columns :
- name : date
data_type : date
description : The date for the month-to-date guest revenue-related metrics.
tests :
- not_null
- unique
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- name : int_core__monthly_guest_payments_history_by_deal
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description : |
This model contains the historic information regarding the guest revenue for each deal id.
It's used for the business KPIs in the view by deal id. Data is aggregated at the last
day of the month, or up to yesterday if it's the current month.
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 guest revenue metrics.
tests :
- not_null
- name : id_deal
data_type : character varying
description : Id of the deal associated to the host.
tests :
- not_null
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- name : int_core__verification_request_completeness
description : |
The `int_core__verification_request_completeness` model allows to determine if a verification request is
completed or not. To achieve it, it encapsulates the logic to determine the different possibilites. Its main
output is the column is_verification_request_complete, but it also provides outputs of the intermediate logic
steps to be used for further modeling, such as determining the completion date.
columns :
- name : id_verification_request
data_type : bigint
description : id of the verification request. It's the unique key for this model.
tests :
- not_null
- unique
- name : expected_verification_count
data_type : int
description : count of verifications that are expected to be passed in order to complete the request.
- name : confirmed_from_same_verification_request_count
data_type : int
description : count of confirmed verifications that its logic is computed from the same verification request.
- name : confirmed_from_previous_verification_requests_count
data_type : int
description : count of confirmed verifications that its logic is computed from previous verification requests.
- name : confirmed_verification_count
data_type : int
description : |
total count of confirmed verifications. Mainly, it's the sum of the confirmed verifications
that come from the same verification request plus the ones that come from previous verifications requests.
- name : is_verification_request_complete
data_type : boolean
description : if the verification request can be considered as completed or not.
- name : used_verification_from_same_verification_request
data_type : boolean
description : |
if the verification request can be considered as completed and has at least one confirmed verification
from the same verification request.
- name : used_verification_from_previous_verification_requests
data_type : boolean
description : |
if the verification request can be considered as completed and has at least one confirmed verification
from a previous verification request.
- name : is_complete_only_from_previous_verification_requests
data_type : boolean
description : |
if the verification request can be considered as completed and all confirmed verifications are from
previous verification requests.
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- name : int_core__verification_request_completed_date
description : |
The `int_core__verification_request_completed_date` model allows to retrieve the time in which the guest
journey, or verification request, was completed. It only considers that a guest journey is completed based
on the positive outcome of the is_verification_complete boolean coming from verification_request_completeness
model.
The completion time is computed as follows :
- Only considering verification requests that have been tagged as completed. From here, we have :
- If the verification request has, at least, one verification linked; the date will be the creation date
of the last verification created linked to that verification request.
To keep in mind : for some cases, the last verification can have updates after the creation, but these
generally happen with very low time differences with respect to the creation date. However, there are
some outliers - mostly linked to admin override - that we're not considering here, since these might
not necessarily be linked to the Guest completing the Guest Journey.
- If the verification request does not have any verification linked; we assume an automatic completion.
In this case, we use the time from which the verification request was created.
For some cases, it is possible that this logic still generates some completed times that are actually
before a user usage of the link. For these cases, we do an override and we apply the used_link_at_utc
as the completed time. To account for this cases, check the boolean column
is_completed_at_overriden_with_used_link_at.
In summary, the guest journey completion time provided here is an estimation.
Finally, this model only contains those request that have been completed, so keep it in mind when joining this
table.
columns :
- name : id_verification_request
data_type : bigint
description : id of the completed verification request. It's the unique key for this model.
tests :
- not_null
- unique
- name : estimated_completed_at_utc
data_type : timestamp
description : estimated timestamp of when the verification request was completed.
- name : estimated_completed_date_utc
data_type : date
description : estimated date from the timestamp of when the verification request was completed.
- name : is_completed_at_overriden_with_used_link_at
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data_type : boolean
description : >
boolean indicating if the estimated dates have been overriden with the
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used link since the initial computation was still considering an end
date before a starting date.
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- name : int_core__verification_payments
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description : >-
A simplified table that holds guest journey payments with details around
when they happen, what service was being paid, what was the related
verification request, etc.
Currency rates are converted to GBP with our simple exchange rates view.
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columns :
- name : id_verification_to_payment
data_type : bigint
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description : Unique ID for the relation between the payment verification
and the payment at hand.
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tests :
- unique
- not_null
- name : id_payment
data_type : bigint
description : Unique ID for the payment itself.
tests :
- unique
- not_null
- name : is_refundable
data_type : boolean
- name : created_at_utc
data_type : timestamp without time zone
- name : updated_at_utc
data_type : timestamp without time zone
- name : payment_due_at_utc
data_type : timestamp without time zone
tests :
- not_null
- name : payment_due_date_utc
data_type : date
tests :
- not_null
- name : payment_paid_at_utc
data_type : timestamp without time zone
- name : payment_paid_date_utc
data_type : date
- name : payment_reference
data_type : character varying
- name : refund_due_at_utc
data_type : timestamp without time zone
- name : refund_due_date_utc
data_type : date
- name : payment_refunded_at_utc
data_type : timestamp without time zone
- name : payment_refunded_date_utc
data_type : date
- name : refund_payment_reference
data_type : character varying
- name : id_guest_user
data_type : character varying
- name : id_verification
data_type : bigint
- name : id_verification_request
data_type : bigint
- name : verification_payment_type
data_type : character varying
- name : amount_in_txn_currency
data_type : numeric
tests :
- not_null
- name : currency
data_type : character varying
tests :
- not_null
- name : amount_in_gbp
data_type : numeric
tests :
- not_null
- name : payment_status
data_type : character varying
- name : notes
data_type : character varying
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- name : int_core__country
description : |
This model contains information regarding countries, such as codes,
names and preferred currencies
columns :
- name : id_country
data_type : bigint
description : id of the country. It's the unique key for this model.
tests :
- not_null
- unique
- name : iso_2
data_type : char(2)
description : ISO 3166-1 alpha-2 country code. Cannot be null.
tests :
- not_null
- name : iso_3
data_type : char(3)
description : |
ISO 3166-1 alpha-3 country code. Some countries can have this value as not set,
therefore it's nullable.
- name : country_name
data_type : character varying
description : name of the country. Cannot be null.
tests :
- not_null
- name : iso_num_code
data_type : int
description : |
ISO 3166-1 numeric code. Usually it's 3 digits, but since it's categorised as
an integer, the preceding zeros are removed. Nullable.
- name : phone_code
data_type : int
description : |
Phone code prefix for a given country. Can contain default / duplicated values.
- name : id_preferred_currency
data_type : int
description : |
Id of the preferred currency for a given country. Might not be the only currency
used in the country, it's just the preferred one.
tests :
- not_null
- name : preferred_currency_name
data_type : character varying
description : |
Currency name of the preferred currency for a given country.
tests :
- not_null
- name : preferred_iso4217_code
data_type : char(3)
description : |
Three-letter code assigned to the preferred currency for a given country by the ISO.
These codes are part of the ISO 4217 standard.
tests :
- not_null
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- name : int_core__accommodation
description : |
This model contains information regarding accommodations, also known as listings.
It contains information regarding the host this accommodation is linked to,
the geographic details, the preferred currency according to the country, details about
the listing itself (floors, bedrooms, etc) and time-related information of when the
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listing was created.
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columns :
- name : id_accommodation
data_type : bigint
description : Id of the accommodation or listing. It's the unique key for this model.
tests :
- not_null
- unique
- name : id_user_host
data_type : character varying
description : The unique ID for the host. Can be null.
- name : id_payment_validation_set
data_type : bigint
description : Id of the payment validation set linked to a listing. Can be null.
- name : friendly_name
data_type : character varying
- name : country_iso_2
data_type : char(2)
description : ISO 3166-1 alpha-2 country code where the listing is located.
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- name : country_name
data_type : character varying
description : Name of the country where the listing is located.
- name : country_preferred_currency_code
data_type : char(3)
description : |
Three-letter code assigned to the preferred currency for a given country by the ISO.
These codes are part of the ISO 4217 standard. Keep in mind this are preferred, not
necessarily the actual currency.
- name : is_active
data_type : boolean
description : |
Boolean to indicate if the listing is active or not. If false, this is considered as a
hard deactivation - meaning no more bookings can be assigned to this listing. However,
even if a listing is active, that does not necessarily mean that it's receiving bookings.
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Do not confuse this column with the lifecycle activity of a listing that is computed in
int_core__mtd_accommodation_lifecycle.
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- name : town
data_type : character varying
- name : postcode
data_type : character varying
- name : address_line_1
data_type : character varying
- name : address_line_2
data_type : character varying
- name : verification_level
data_type : integer
- name : floor_area
data_type : integer
- name : number_of_floors
data_type : integer
- name : number_of_bedrooms
data_type : integer
- name : number_of_bathrooms
data_type : integer
- name : number_of_other_rooms
data_type : integer
- name : construction_details
data_type : character varying
- name : created_at_utc
data_type : timestamp
description : Timestamp of when the listing was created. Cannot be null.
tests :
- not_null
- name : created_date_utc
data_type : date
description : Date of when the listing was created
- name : updated_at_utc
data_type : timestamp
description : Timestamp of when the listing was last updated according to the backend.
- name : updated_date_utc
data_type : date
description : Date of when the listing was last updated according to the backend.
- name : dwh_extracted_at_utc
data_type : timestamp
description : Timestamp of when the accommodation record was extracted from the backend into the DWH.
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- name : int_core__mtd_accommodation_lifecycle
description : |
This model contains the historic information regarding the lifecycle of accommodations, also known as
listings. The information regarding the booking-related time allows for the current status of any listing
regarding its activity. This information is encapsulated in the following columns :
accommodation_lifecycle_state : contains one of the following states
- 01-New : Listings that have been created in the current month, without bookings
- 02-Never Booked : Listings that have been created before the current month, without bookings.
- 03-First Time Booked : Listings that have been booked for the first time in the current month.
- 04-Active : Listings that have booking activity in the past 12 months (that are not FTB nor reactivated)
- 05-Churning : Listings that are becoming inactive because of lack of bookings in the past 12 months
- 06-Inactive : Listings that have not had a booking for more than 12 months.
- 07-Reactivated : Listings that have had a booking in the current month that were inactive or churning before.
- Finally, if none of the logic applies, which should not happen, null will be set and a dbt alert will raise.
Since the states of Active, First Time Booked and Reactivated indicate certain booking activity and are
mutually exclusive, the model also provides information of the recency of the bookings by the following
booleans :
- has_been_booked_within_current_month : If a listing has had a booking created in the current month
- has_been_booked_within_last_6_months : If a listing has had a booking created in the past 6 months
- has_been_booked_within_last_12_months : If a listing has had a booking created in the past 12 months
Note that if a listing has had a booking created in a given month, all 3 columns will be true. Similarly,
if the last booking created to a listing was 5 months ago, only the column has_been_booked_in_1_month
will be false; while the other 2 will be true.
tests :
- dbt_utils.unique_combination_of_columns :
combination_of_columns :
- date
- id_accommodation
columns :
- name : date
data_type : date
description : The date for the month-to-date. Information is inclusive to the date displayed.
tests :
- not_null
- name : id_accommodation
data_type : bigint
description : Id of the accommodation or listing.
tests :
- not_null
- name : creation_date_utc
data_type : date
description : Date of when the listing was created.
- name : first_time_booked_date_utc
data_type : date
description : |
Date of the first booking created for a given listing. Can be null if the listing
has never had a booking associated with it.
- name : last_time_booked_date_utc
data_type : date
description : |
Date of the last booking created for a given listing. Can be null if the listing
has never had a booking associated with it. Can be the same as first_time_booked_date_utc
if the listing only had 1 booking in its history.
- name : second_to_last_time_booked_date_utc
data_type : date
description : |
Date of the second-to-last booking created for a given listing, meaning the creation
date of the booking that precedes the last one. It's relevant for the reactivation computation
on the lifecycle. Can be null if the listing has never had a booking associated with it or if
the listing only had 1 booking in its history.
- name : accommodation_lifecycle_state
data_type : character varying
description : |
Contains the lifecycle state of a Listing. The accepted values are :
01 -New, 02-Never Booked, 03-First Time Booked, 04-Active, 05-Churning, 06-Inactive,
07 -Reactivated. Failing to implement the logic will result in alert.
tests :
- not_null
- name : has_been_booked_within_current_month
data_type : boolean
description : If the listing has had a booking created in the current month.
- name : has_been_booked_within_last_6_months
data_type : boolean
description : If the listing has had a booking created in the past 6 months.
- name : has_been_booked_within_last_12_months
data_type : boolean
description : If the listing has had a booking created in the past 12 months.
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- name : int_core__mtd_accommodation_segmentation
description : |
This model contains the historic information regarding the customer segmentation based on active accommodations,
also known as listings. This model depends on the int_core__mtd_accommodation_lifecycle, thus the time granularity
is the same as for any mtd model.
The segmentation is applied at deal level, not at user host level. This has the drawback that hosts without a
deal assigned won't be considered in this segmentation. However, it correctly categorises customers based on the
number of listings across the different user hosts each deal can have.
The segmentation is available in the field active_accommodations_per_deal_segmentation. It considers listings that
have been booked within the last 12 months. Thus, it means it only considers the lifecycle states of
03-First Time Booked, 04-Active and 07-Reactivated. The segments are the following :
- '0'
- '1-5'
- '6-20'
- '21-60'
- '61+'
These segments are inspired from the ones RevOps team uses, but the associated deals and listings volume will differ
since the listing activity logic considered is different.
tests :
- dbt_utils.unique_combination_of_columns :
combination_of_columns :
- date
- id_deal
columns :
- name : date
data_type : date
description : The date for the month-to-date. Information is inclusive to the date displayed.
tests :
- not_null
- name : id_deal
data_type : character varying
description : Id of the deal associated to the host.
tests :
- not_null
- name : active_accommodations_per_deal_segmentation
data_type : character varying
description : The segmentation based on the number of listings booked in the last 12 months.
tests :
- not_null
- accepted_values :
values :
- '0'
- '1-5'
- '6-20'
- '21-60'
- '61+'
- name : accommodations_booked_in_12_months
data_type : integer
description : Informative field of the actual number of listings booked in the last 12 months
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- name : int_core__mtd_deal_lifecycle
description : |
This model contains the historic information regarding the lifecycle of hosts, at deal level.
The information regarding the booking-related time allows for the current status of any
deal regarding its activity. This information is encapsulated in the following columns :
deal_lifecycle_state : contains one of the following states
- 01-New : Deals that have been created in the current month, without bookings
- 02-Never Booked : Deals that have been created before the current month, without bookings.
- 03-First Time Booked : Deals that have been booked for the first time in the current month.
- 04-Active : Deals that have booking activity in the past 12 months (that are not FTB nor reactivated)
- 05-Churning : Deals that are becoming inactive because of lack of bookings in the past 12 months
- 06-Inactive : Deals that have not had a booking for more than 12 months.
- 07-Reactivated : Deals that have had a booking in the current month that were inactive or churning before.
- Finally, if none of the logic applies, which should not happen, null will be set and a dbt alert will raise.
Since the states of Active, First Time Booked and Reactivated indicate certain booking activity and are
mutually exclusive, the model also provides information of the recency of the bookings by the following
booleans :
- has_been_booked_within_current_month : If a deal has had a booking created in the current month
- has_been_booked_within_last_6_months : If a deal has had a booking created in the past 6 months
- has_been_booked_within_last_12_months : If a deal has had a booking created in the past 12 months
Note that if a deal has had a booking created in a given month, all 3 columns will be true. Similarly,
if the last booking created to a deal was 5 months ago, only the column has_been_booked_in_1_month
will be false; while the other 2 will be true.
tests :
- dbt_utils.unique_combination_of_columns :
combination_of_columns :
- date
- id_deal
columns :
- name : date
data_type : date
description : The date for the month-to-date. Information is inclusive to the date displayed.
tests :
- not_null
- name : id_deal
Merged PR 2164: Adding booking metrics by deal id for business kpis
This is a first approach to compute some easy metrics for the "deal" based business kpis. At this stage, it contains the information of bookings (created, checkout, cancelled) per deal and month, including both historic months as well as the current one. This do not contain MTD computation because it's overkill to do a MTD at deal level (+ we have 1k deals, so scalability can become a problem in the future)
Models:
- **int_dates_by_deal**: simple model that reads from **int_dates** and just joins it with **unified_users** to retrieve the deals. It will be used as the 'source of truth' for which deals should be considered in a given month, basically, since the first host associated to a deal is created (not necessarily booked)
- **int_core__monthly_booking_history_by_deal**: it contains the history of bookings per deal id in a monthly basis. It should be easy enough to integrate here, in the future and if needed, B2B macro segmentation.
In terms of performance, comparing the model **int_core__monthly_booking_history_by_deal** and **int_core__mtd_booking_metrics** you'll see that I removed the joined with the **int_dates_xxx** in the CTEs. This is because I want to avoid a double join of date & deal that I tried and I stopped after 5 min running. Since this computation is in a monthly basis - no MTD - it's easy enough to just apply the **int_dates_by_deal** on the last part of the query. With this approach, it runs in 7 seconds.
Related work items: #17689
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data_type : character varying
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description : Id of the deal associated to the host.
tests :
- not_null
- name : creation_date_utc
data_type : date
description : Date of when the first host associated to that deal was created.
- name : first_time_booked_date_utc
data_type : date
description : |
Date of the first booking created for a given deal. Can be null if the deal
has never had a booking associated with it.
- name : last_time_booked_date_utc
data_type : date
description : |
Date of the last booking created for a given deal. Can be null if the deal
has never had a booking associated with it. Can be the same as first_time_booked_date_utc
if the deal only had 1 booking in its history.
- name : second_to_last_time_booked_date_utc
data_type : date
description : |
Date of the second-to-last booking created for a given deal, meaning the creation
date of the booking that precedes the last one. It's relevant for the reactivation computation
on the lifecycle. Can be null if the deal has never had a booking associated with it or if
the deal only had 1 booking in its history.
- name : deal_lifecycle_state
data_type : character varying
description : |
Contains the lifecycle state of a deal. The accepted values are :
01 -New, 02-Never Booked, 03-First Time Booked, 04-Active, 05-Churning, 06-Inactive,
07 -Reactivated. Failing to implement the logic will result in alert.
tests :
- not_null
- name : has_been_booked_within_current_month
data_type : boolean
description : If the deal has had a booking created in the current month.
- name : has_been_booked_within_last_6_months
data_type : boolean
description : If the deal has had a booking created in the past 6 months.
- name : has_been_booked_within_last_12_months
data_type : boolean
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description : If the deal has had a booking created in the past 12 months.
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- name : int_core__check_in_cover_users
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description :
This model contains information about hosts that offer check in cover.
It has basic information on the users like name, phone, email or joined date.
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This model is restricted to active user so it doesn't include historical data
like users that had check-in cover but are currently inactive.
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columns :
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- name : id_user_host
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data_type : character varying
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description : Unique id value for the user
tests :
- not_null
- unique
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- name : id_deal
data_type : character varying
description : ""
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- name : last_name
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data_type : character varying
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description : Last name of the user
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- name : user_name
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data_type : character varying
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description : User name of the user
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- name : first_name
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data_type : character varying
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description : First name of the user
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- name : host_email
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data_type : character varying
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description : Email of the user
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- name : phone_number
data_type : character varying
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description : Phone number of the user
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- name : joined_at_utc
data_type : timestamp without time zone
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description : Date and time the user joined
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- name : joined_date_utc
data_type : date
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description : Date the user joined
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- name : check_in_cover_added_date_utc
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data_type : date
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description :
Date the user first included check-in cover
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- name : billing_town
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data_type : character varying
description : ""
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- name : company_name
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data_type : character varying
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description : ""
- name : int_core__guest_satisfaction_responses
description :
This model contains information on guests satisfaction survey responses,
it contains some basic information on the guests, a rating of their experience
and some comments on it.
It also includes information on the services provided by Superhog that they payed for.
columns :
- name : id_verification_request
data_type : bigint
description : Unique id value for the verification request
tests :
- not_null
- unique
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- name : id_user_guest
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data_type : character varying
description : Unique id value for the guest
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- name : guest_email
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data_type : character varying
description : Guest email
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- name : verification_request_booking_source
data_type : text
description :
Source type of host of the booking, this could be either;
- PMS
- OSL
- API/MANUAL
tests :
- not_null
- accepted_values :
values :
- 'PMS'
- 'OSL'
- 'API/MANUAL'
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- name : experience_rating
data_type : bigint
description :
Guest rating of their experience with Superhog from 1 to 5
- name : guest_comments
data_type : character varying
description :
Guest comments on their experience with Superhog
- name : is_contactable
data_type : boolean
description : |
True if the guest allows to be contacted for more feedback
- name : created_at_utc
data_type : timestamp without time zone
description :
Date and time of response creation
- name : updated_at_utc
data_type : timestamp without time zone
description :
Date and time of last update of response
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- name : selected_payment_option
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data_type : character varying
description : ""
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- name : date_of_birth
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data_type : numeric
description : ""
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- name : age_of_guest
data_type : numeric
description : ""
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- name : has_check_in_cover_payment
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data_type : boolean
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description : |
True if guest payed for check-in cover
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- name : has_waiver_payment
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data_type : boolean
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description : |
True if guest payed the waiver
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- name : has_deposit_payment
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data_type : boolean
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description : |
True if guest payed the deposit
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- name : has_fee_payment
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data_type : boolean
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description : |
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True if guest payed the fee
- name : int_core__verification_request_booking_source
description :
This model contains information on verification requests
and the category type of host that manages the associated
booking.
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For PMS (Property Manager System) we use the id_integration
from stg_core__booking, if it isn't Null then the host is PMS type.
For OSL (One Step Link) we use the id_one_step_link from
stg_core__verification_request, similarly if it isn't Null then
the host is OSL type.
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Finally if both id_integration and id_one_step_link are Null,
then we classify them as API/MANUAL. (At this point we can't
differentiate between these 2 categories so for now we keep
them together)
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columns :
- name : id_verification_request
data_type : bigint
description :
Id value for the verification request, there can be more
than 1 record for each verification request since they can
be associated to more than 1 booking
tests :
- not_null
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- unique
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- name : created_at_utc
data_type : timestamp without time zone
description :
Date and time of creation of the verification request
- name : created_date_utc
data_type : date
description :
Date of creation of the verification request
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- name : verification_request_booking_source
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data_type : text
description :
Source type of host of the booking, this could be either;
- PMS
- OSL
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- API/MANUAL
tests :
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- not_null
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- accepted_values :
values :
- 'PMS'
- 'OSL'
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- 'API/MANUAL'
- name : int_core__verification_requests
description :
This is a table that shows all guest journey from our guests users with
each record matching each guest journey.
It holds information about the guests like name, email, phone, etc.., as
well as dates regarding the process of the guest journey like when it
was started or finished.
columns :
- name : id_verification_request
data_type : bigint
description :
Unique, incremental, internal ID for the related verification
request.
- name : uuid_verification_request
data_type : text
description : uuid for the related verification request.
- name : id_verification_set
data_type : bigint
description : ""
- name : id_superhog_verified_set
data_type : bigint
description : ""
- name : id_payment_validation_set
data_type : bigint
description : ""
- name : id_user_guest
data_type : character varying
description : Unique, incremental, internal ID for the guest user.
- name : id_user_host
data_type : character varying
description : Unique, incremental, internal ID for the host user.
- name : is_verification_request_complete
data_type : boolean
description : True if the verification request is considered
complete, AKA the guest has finished the full guest journey.
- name : verification_url
data_type : character varying
description : ""
- name : callback_url
data_type : character varying
description : ""
- name : redirect_url
data_type : character varying
description : ""
- name : logo
data_type : character varying
description : ""
- name : guest_email
data_type : character varying
description : The email of the guest.
- name : last_name
data_type : character varying
description : The last name of the guest.
- name : first_name
data_type : character varying
description : The first name of the guest.
- name : guest_phone_number
data_type : character varying
description : The phone number of the guest.
- name : telephone_code
data_type : character varying
description : The telephone code of the guest.
- name : guest_phone_number_2
data_type : character varying
description : ""
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- name : verification_estimated_started_at_utc
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data_type : timestamp without time zone
description :
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The estimated date and time at which the guest started the guest journey.
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- name : verification_estimated_started_date_utc
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data_type : date
description :
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The estimated date on which the guest started the guest journey.
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- name : verification_estimated_completed_at_utc
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data_type : timestamp without time zone
description :
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The estimated date and time at which the guest finished the guest journey.
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- name : verification_estimated_completed_date_utc
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data_type : date
description :
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The estimated date on which the guest finished the guest journey.
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- name : link_used_at_utc
data_type : timestamp without time zone
description :
The date and time at which the guest used the link for the verification.
- name : link_used_date_utc
data_type : date
description :
The date on which the guest used the link for the verification.
- name : expire_at_utc
data_type : timestamp without time zone
description :
The date and time at which the link for the verification expires.
- name : expire_date_utc
data_type : date
description :
The date on which the link for the verification expires.
- name : is_deleted
data_type : boolean
description : |
True if the link for verification expired before finishing the
verification.
- name : redirect_name
data_type : character varying
description : ""
- name : id_one_step_link
data_type : bigint
description : ""
- name : verification_request_booking_source
data_type : text
description :
Source type of host of the booking, this could be either;
- PMS
- OSL
- API/MANUAL
tests :
- not_null
- accepted_values :
values :
- 'PMS'
- 'OSL'
- 'API/MANUAL'
- name : success_message
data_type : character varying
description : ""
- name : summary
data_type : character varying
description : ""
- name : rejection_reason
data_type : character varying
description :
Reason as to why the guest was rejected.
- name : has_switched_to_mobile
data_type : boolean
description : |
True if the guest changed has switched to mobile
during the verification process.
- name : is_verifier_rejected
data_type : boolean
description : ""
- name : config
data_type : character varying
description : ""
- name : metadata
data_type : character varying
description : ""
- name : created_at_utc
data_type : timestamp without time zone
description :
The date and time at which the verification process was created.
- name : created_date_utc
data_type : date
description :
The date on which the verification process was created.
- name : updated_at_utc
data_type : timestamp without time zone
description :
The date and time at which the last update on the entry happened.
- name : updated_date_utc
data_type : date
description :
The date on which the last update on the entry happened.
- name : dwh_extracted_at_utc
data_type : timestamp with time zone
description : ""
- name : int_core__bookings
description : ""
columns :
- name : id_booking
data_type : bigint
description : "The unique, Superhog generated id for this booking."
tests :
- unique
- not_null
- name : id_user_guest
data_type : character varying
description : "The unique, Superhog generated id for the guest"
- name : id_user_host
data_type : character varying
description : "The unique, Superhog generated id for the host"
- name : id_integration
data_type : character varying
description : ""
- name : id_accommodation
data_type : bigint
description : "The ID of the booked listing."
- name : id_booking_source
data_type : bigint
description : ""
- name : id_verification_request
data_type : bigint
description : "Id value for the verification request, there can be more than 1 record for each verification request since they can be associated to more than 1 booking"
- name : verification_request_booking_source
data_type : text
description :
Source type of host of the booking, this could be either;
- PMS
- OSL
- API/MANUAL
tests :
- not_null
- accepted_values :
values :
- 'PMS'
- 'OSL'
- 'API/MANUAL'
- name : id_staging_host_booking
data_type : bigint
description : ""
- name : is_duplicate_booking
data_type : boolean
description :
A flag that identifies whether the booking is a duplicate.
A booking is considered a duplicate if there's an older booking with the same user,
accomodation and check-in date. If there are two or more bookings with the same user,
accomodation and check-in date, the oldest one will have False as a value in this field,
and the other ones will have True as a value in this Failed."
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Put simply, if you don't want to receive duplicates, filter this field to False.
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- name : is_duplicating_booking_with_id
data_type : bigint
description :
"If is_duplicate_booking is True then gives id_booking"
- name : booking_state
data_type : character varying
description : ""
- name : check_in_at_utc
data_type : timestamp without time zone
description : ""
- name : check_in_date_utc
data_type : date
description : ""
- name : check_out_at_utc
data_type : timestamp without time zone
description : ""
- name : check_out_date_utc
data_type : date
description : ""
- name : check_in_sits_in_future
data_type : boolean
description : ""
- name : check_out_sits_in_future
data_type : boolean
description : ""
- name : booking_fee_local
data_type : numeric
description : "The fee to apply to the booking, in host currency."
- name : booking_fee_charge_at_utc
data_type : timestamp without time zone
description : The point in time in which the booking should be invoiced.
This could be the check-in date of the booking or the date in which the guest verification
started, depending on the billing settings of the host.
- name : booking_fee_charge_date_utc
data_type : date
description : The date in which the booking should be invoiced.
This could be the check-in date of the booking or the date in which the guest verification
started, depending on the billing settings of the host.
- name : summary
data_type : character varying
description : ""
- name : guest_email
data_type : character varying
description : ""
- name : guest_last_name
data_type : character varying
description : ""
- name : guest_first_name
data_type : character varying
description : ""
- name : guest_telephone
data_type : character varying
description : ""
- name : additional_guests
data_type : character varying
description : ""
- name : unsubscribe_verification_reminder
data_type : boolean
description : ""
- name : created_at_utc
data_type : timestamp without time zone
description : "Date and time of creation of the verification request"
- name : created_date_utc
data_type : date
description : "Date of creation of the verification request"
- name : updated_at_utc
data_type : timestamp without time zone
description : ""
- name : updated_date_utc
data_type : date
description : ""
- name : dwh_extracted_at_utc
data_type : timestamp with time zone
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description : ""
- name : int_core__check_in_cover_listings
description :
This model contains information about hosts and their listings
that offer check in cover.
It has basic information on the users and listings like country,
town, address and if they are active or not.
This model is restricted to active user so it doesn't include historical
data like users that had check-in cover but are currently inactive.
columns :
- name : id_user_host
data_type : character varying
description : Unique id value for the user
tests :
- not_null
- name : id_deal
data_type : character varying
description : ""
- name : last_name
data_type : character varying
description : Last name of the user
- name : user_name
data_type : character varying
description : User name of the user
- name : first_name
data_type : character varying
description : First name of the user
- name : host_email
data_type : character varying
description : Email of the user
- name : phone_number
data_type : character varying
description : Phone number of the user
- name : joined_at_utc
data_type : timestamp without time zone
description : Date and time the user joined
- name : joined_date_utc
data_type : date
description : Date the user joined
- name : check_in_cover_added_date_utc
data_type : date
description :
Date the user first included check-in cover
- name : billing_town
data_type : character varying
description : ""
- name : company_name
data_type : character varying
description : ""
- name : id_accommodation
data_type : bigint
description : "Id of the accommodation or listing. It's the unique key for this model."
tests :
- not_null
- unique
- name : is_active
data_type : boolean
description : "Boolean to indicate if the accommodation is active or not"
- name : friendly_name
data_type : character varying
description : "Name of the accommodation"
- name : country_name
data_type : character varying
description : "Name of the country where the accommodation is located."
- name : town
data_type : character varying
description : "Town in which the accommodation is located"
- name : postcode
data_type : character varying
description : ""
- name : address_line_1
data_type : character varying
description : ""
- name : check_in_cover_purchased
data_type : bigint
description :
"Count of how many Check-in covers have been
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purchased for this accommodation"
- name : int_core__host_booking_fees
description :
Bookings that have been processed by the Superhog backend.
Each record matches one booking and has information on host
booking fees, when they were charged and the currency used by
the host.
columns :
- name : id_booking
data_type : bigint
description : "The unique, Superhog generated id for this booking."
tests :
- unique
- not_null
- name : id_user_guest
data_type : character varying
description : The UUID of the Superhog user playing the guest role in the booking.
- name : id_user_host
data_type : character varying
description : The UUID of the Superhog user playing the host role in the booking.
tests :
- not_null
- name : id_accommodation
data_type : bigint
description : The ID of the booked listing.
- name : booking_state
data_type : character varying
description :
"State in which the booking is, could be either of the following:
- Approved
- NotApproved
- Cancelled
- Rejected
- NoFlags
- Flagged
- IncompleteInformation"
tests :
- accepted_values :
values :
- 'Approved'
- 'NotApproved'
- 'Cancelled'
- 'Rejected'
- 'NoFlags'
- 'Flagged'
- 'IncompleteInformation'
- name : is_duplicate_booking
data_type : boolean
description : |
A flag that identifies whether the booking is a duplicate.
A booking is considered a duplicate if there's an older booking with the same user,
accomodation and check-in date. If there are two or more bookings with the same user,
accomodation and check-in date, the oldest one will have False as a value in this field,
and the other ones will have True as a value in this Failed."
Put simply, if you don't want to receive duplicates, filter this field to False.
- name : booking_fee_local
data_type : numeric
description : "The fee to apply to the booking, in host currency."
- name : account_currency_iso4217
data_type : character varying
description : "Currency used by host/pm/platform users."
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- name : booking_fee_in_gbp
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data_type : numeric
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description : "The fee to apply to the booking, in GBP"
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- name : booking_fee_charge_at_utc
data_type : timestamp without time zone
description : |
The point in time in which the booking should be invoiced.
This could be the check-in date of the booking or the date in which the guest verification
started, depending on the billing settings of the host.
- name : booking_fee_charge_date_utc
data_type : date
description : |
The date in which the booking should be invoiced.
This could be the check-in date of the booking or the date in which the guest verification
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started, depending on the billing settings of the host.
- name : int_core__user_role
description : |
This model contains the relationship of user has a role.
A User in this table can have 1 or more than 1 roles.
Not all Users in this table appear in the standard user table,
meaning that not all users have a role assigned.
The possible roles are :
- Host
- Platform
- EDeposit
- Guest
- Admin
- KnowYourGuest
- ScreeningAPI
- PropertyVerificationManager
tests :
- dbt_utils.unique_combination_of_columns :
combination_of_columns :
- id_user
- role_name
columns :
- name : id_user
data_type : string
description : |
The identifier of the user. Can be duplicated if it has multiple roles.
tests :
- not_null
- name : role_name
data_type : string
description : The name of the role a user has.
tests :
- accepted_values :
values :
- Host
- Platform
- EDeposit
- Guest
- Admin
- KnowYourGuest
- ScreeningApi
- PropertyVerificationManager
- name : int_core__user_host
description : |
This table provides information of the users that act as Hosts.
A Host needs to be understood in the broad sense of the term. Here host means any
user that acts as a "B2B" client.
The categorisation as a Host is based on the role of the user. Any user that has
any of the following roles will be considered as a Host in this table :
- Host
- Platform
- EDeposit
- KnowYourGuest
- ScreeningAPI
- PropertyVerificationManager
columns :
- name : id_user_host
data_type : character varying
description : The unique user ID for the Host.
tests :
- not_null
- unique
- name : id_account_type
data_type : integer
description : |
Account type ID. Can be null and might be not up-to-date.
- name : id_billing_country
data_type : integer
description : |
ID of the country in which the Host is billed.
In some cases it's null.
- name : account_currency_iso4217
data_type : string
description : |
3 character currency code linked to the account.
In some cases it's null.
- name : user_code
data_type : integer
description : |
A code identifying users.
- name : first_name
data_type : string
description : |
First name of the Host.
- name : last_name
data_type : string
description : |
Last name of the Host.
- name : company_name
data_type : string
description : |
Name of the company. In some cases, it's the same
as the first_name, the last_name, a concatenation of
both, or something different. Can be null and empty.
- name : email
data_type : string
description : |
Electronic mail of the Host.
- name : id_deal
data_type : string
description : |
Main identifier of the B2B clients. A Deal can have multiple Hosts.
A Host can have only 1 Deal or no Deal at all. This field can be null.
- name : joined_at_utc
data_type : timestamp
description : |
Timestamp of when the Host user joined Superhog.
- name : joined_date_utc
data_type : date
description : |
Date of when the Host user joined Superhog.
- name : created_date_utc
data_type : date
description : |
Date of when the Host user was created in our systems.
- name : updated_date_utc
data_type : date
description : |
Date of the last time the information of the Host was updated
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in our systems.