lmiranda
457bb49395
Sprint 4 implementation: Loaders: - base.py: Session management, bulk insert, upsert utilities - dimensions.py: Load time, district, zone, neighbourhood, policy dimensions - trreb.py: Load TRREB purchase data to fact_purchases - cmhc.py: Load CMHC rental data to fact_rentals dbt Project: - Project configuration (dbt_project.yml, packages.yml) - Staging models for all fact and dimension tables - Intermediate models with dimension enrichment - Marts: purchase analysis, rental analysis, market summary Closes #16 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
58 lines
1.2 KiB
SQL
58 lines
1.2 KiB
SQL
-- Intermediate: Annual rental data enriched with dimensions
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-- Joins rentals with time and zone dimensions for analysis
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with rentals as (
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select * from {{ ref('stg_cmhc__rentals') }}
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),
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time_dim as (
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select * from {{ ref('stg_dimensions__time') }}
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),
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zone_dim as (
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select * from {{ ref('stg_dimensions__cmhc_zones') }}
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),
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enriched as (
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select
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r.rental_id,
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-- Time attributes
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t.date_key,
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t.full_date,
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t.year,
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t.month,
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t.quarter,
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-- Zone attributes
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z.zone_key,
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z.zone_code,
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z.zone_name,
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-- Bedroom type
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r.bedroom_type,
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-- Metrics
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r.rental_universe,
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r.avg_rent,
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r.median_rent,
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r.vacancy_rate,
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r.availability_rate,
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r.turnover_rate,
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r.year_over_year_rent_change,
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r.reliability_code,
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-- Calculated metrics
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case
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when r.rental_universe > 0 and r.vacancy_rate is not null
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then round(r.rental_universe * (r.vacancy_rate / 100), 0)
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else null
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end as vacant_units_estimate
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from rentals r
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inner join time_dim t on r.date_key = t.date_key
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inner join zone_dim z on r.zone_key = z.zone_key
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)
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select * from enriched
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