85 lines
3 KiB
YAML
85 lines
3 KiB
YAML
# Name your project! Project names should contain only lowercase characters
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# and underscores. A good package name should reflect your organization's
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# name or the intended use of these models
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name: "dwh_dbt"
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version: "1.0.0"
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config-version: 2
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# This setting configures which "profile" dbt uses for this project.
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profile: "dwh_dbt"
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# These configurations specify where dbt should look for different types of files.
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# The `model-paths` config, for example, states that models in this project can be
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# found in the "models/" directory. You probably won't need to change these!
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model-paths: ["models"]
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analysis-paths: ["analyses"]
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test-paths: ["tests"]
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seed-paths: ["seeds"]
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macro-paths: ["macros"]
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snapshot-paths: ["snapshots"]
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clean-targets: # directories to be removed by `dbt clean`
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- "target"
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- "dbt_packages"
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# Configuring models
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# Full documentation: https://docs.getdbt.com/docs/configuring-models
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# In this example config, we tell dbt to build all models in the example/
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# directory as views. These settings can be overridden in the individual model
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# files using the `{{ config(...) }}` macro.
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models:
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+unlogged: true
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# ^ This makes all the tables created by dbt be unlogged. This is a Postgres
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# specific setting that we activate for performance. It has deep implications
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# on how Postgres handles tables and is typically considered crazy risky, but
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# it's very fitting for our needs. You can read more here:
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# https://www.crunchydata.com/blog/postgresl-unlogged-tables
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dwh_dbt:
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+post-hook:
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sql: "VACUUM ANALYZE {{ this }}"
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transaction: false
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# ^ This makes dbt run a VACUUM ANALYZE on the models after building each.
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# It's pointless for views, but it doesn't matter because Postgres fails
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# silently withour raising an unhandled exception.
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staging:
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+materialized: table
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+schema: staging
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intermediate:
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+materialized: view
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+schema: intermediate
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reporting:
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+materialized: table
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+schema: reporting
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seeds:
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dwh_dbt:
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schema: staging
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vars:
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"dbt_date:time_zone": "Europe/London"
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# A general cutoff date for relevancy. Many models assume this to be the point
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# in time after which they should work.
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"start_date": "'2020-01-01'"
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# KPIs Start Date. This is the date from which we start calculating KPIs.
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"kpis_start_date": "'2022-04-01'"
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# New Dash First Invoicing Date. This is the first date considered for New Dash invoicing.
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"new_dash_first_invoicing_date": "'2024-12-31'"
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# A distant future date to use as a default when cutoff values are missing.
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"end_of_time": "'2050-12-31'"
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# Booking state variables
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# States should be strings in capital letters. Models need to force an upper()
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"cancelled_booking_state": "'CANCELLED'"
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"approved_booking_state": "'APPROVED'"
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# Payment state variables
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# States should be strings in capital letters. Models need to force an upper()
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"paid_payment_state": "'PAID'"
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# Protection service state variables
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# States should be strings in capital letters. Models need to force an upper()
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"default_service": "'BASIC SCREENING'"
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